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Executing Semantics:
Towards Networked Science

           Anita de Waard
     Disruptive Technologies Director
        Elsevier Labs, Jericho, VT
Three problems, some solutions,
    and ideas for the future




                            2
Three problems, some solutions,
    and ideas for the future
• Three examples where scientific discourse
  falls short: why we need more context




                                        2
Three problems, some solutions,
    and ideas for the future
• Three examples where scientific discourse
  falls short: why we need more context
• Some projects that are modeling context now




                                        2
Three problems, some solutions,
    and ideas for the future
• Three examples where scientific discourse
  falls short: why we need more context
• Some projects that are modeling context now
• What we really need: networked knowledge




                                        2
1. Lexapro for adolescents




                         3
1. Lexapro for adolescents




                         3
1. Lexapro for adolescents




                         3
1. Lexapro for adolescents




                         3
4
4
5
5
5
5
6
6
6
6
Problem #1: Knowledge is not
   connected or tracable




                          7
Problem #1: Knowledge is not
     connected or tracable
• How can we scale up the 1-to-1 interactions on
  efficacy and side effects happening today?




                                              7
Problem #1: Knowledge is not
     connected or tracable
• How can we scale up the 1-to-1 interactions on
  efficacy and side effects happening today?
• How do we know who is speaking in a patient
  forum?




                                              7
Problem #1: Knowledge is not
     connected or tracable
• How can we scale up the 1-to-1 interactions on
  efficacy and side effects happening today?
• How do we know who is speaking in a patient
  forum?
• How to we get scientific knowledge in on this?




                                              7
Problem #1: Knowledge is not
     connected or tracable
• How can we scale up the 1-to-1 interactions on
  efficacy and side effects happening today?
• How do we know who is speaking in a patient
  forum?
• How to we get scientific knowledge in on this?
• How do we know who paid for knowledge?




                                              7
Problem #1: Knowledge is not
     connected or tracable
• How can we scale up the 1-to-1 interactions on
  efficacy and side effects happening today?
• How do we know who is speaking in a patient
  forum?
• How to we get scientific knowledge in on this?
• How do we know who paid for knowledge?
• If a study is sponsored, how do you refer back to
  sources that link out to it?


                                               7
2. Drug-drug interactions




                            8
2. Drug-drug interactions
• Drug Interaction Knowledge Base




                                    8
2. Drug-drug interactions
• Drug Interaction Knowledge Base
• Problem: how to integrate knowledge from
  various repositories and data stores into a
  single source




                                           8
2. Drug-drug interactions
• Drug Interaction Knowledge Base
• Problem: how to integrate knowledge from
  various repositories and data stores into a
  single source
• One of the main stumbling blocks: the way
  we record experiments in prose:




                                           8
E.g. Moltke et al, 1999:




                           9
E.g. Moltke et al, 1999:
   After normalization for hepatic abundance, relative
   contributions to net intrinsic clearance were 37% for
   CYP2C19, 28% for CYP2D6, and 35% for CYP3A4.
                              All samples were of the CYP2D6 and CYP2C19 normal
                              metabolizer phenotype based on prior in vitro
                              phenotyping studies.
    Average relative in vivo abundances, equivalent to the relative activity
    factors, were estimated using methods described in detail
    previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999,
    2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).
                R-CT and its metabolites, studied using the same procedures, had
                properties very similar to those of the corresponding S-enantiomers.
Based on established index reactions, S-CT and S-DCT were negligible
inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
weakly inhibited CYP2D6 (IC50 = 70–80 µM)
                           S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM),
                           CYP2D6 (Km = 29 µM), and CYP3A4 (Km = 588 µM).
The potential inhibitory effect of the stereoisomers of CT, DCT, and DDCT on the activity of
six human cytochromes was evaluated using index reactions and methods as follows (Table
1): CYP1A2, phenacetin (100 µM) to acetaminophen (von Moltke et al.,1996a;
Venkatakrishnan et al., 1998b); CYP2C9, tolbutamide (100 µM) to hydroxytolbutamide
                                                                                 9
(Venkatakrishnan et al., 1998c); CYP2C19, S-mephenytoin (25 µM) to 4′-OH-mephenytoin
Problem #2:
Knowledge is not actionable




                         10
Problem #2:
     Knowledge is not actionable
• Self-reference:




                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:




                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.




                                                                           10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:




                                                                           10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:




                                                                           10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).




                                                                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).




                                                                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).

• Ways of describing meant for human eyes




                                                                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).

• Ways of describing meant for human eyes
    Based on established index reactions, S-CT and S-DCT were negligible
    inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
    weakly inhibited CYP2D6 (IC50 = 70–80 µM)




                                                                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).

• Ways of describing meant for human eyes
    Based on established index reactions, S-CT and S-DCT were negligible
    inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
    weakly inhibited CYP2D6 (IC50 = 70–80 µM)




                                                                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).

• Ways of describing meant for human eyes
    Based on established index reactions, S-CT and S-DCT were negligible
    inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
    weakly inhibited CYP2D6 (IC50 = 70–80 µM)




                                                                              10
Problem #2:
     Knowledge is not actionable
• Self-reference:
    R-CT and its metabolites, studied using the same procedures, had
    properties very similar to those of the corresponding S-enantiomers.

• Reference to external data sources:
    Average relative in vivo abundances equivalent to the relative activity
    factors, were estimated using methods described in detail previously
    (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
    von Moltke et al., 1999 a,b; Störmer et al., 2000).

• Ways of describing meant for human eyes
    Based on established index reactions, S-CT and S-DCT were negligible
    inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
    weakly inhibited CYP2D6 (IC50 = 70–80 µM)

• Many statements wrapped into one:
    S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM), CYP2D6 (Km
    = 29 µM), and CYP3A4 (Km = 588 µM).                           10
3. NIF Antibody Study




                                                 Maryann Martone, Jan 2012:
   2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
 in studies




                                                                 Maryann Martone, Jan 2012:
                   2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
  in studies
• Antibodies are a major source of experimental variability:
  –Same antibody can give very different results
  –Different antibodies to the same protein can give very
    different results




                                                                 Maryann Martone, Jan 2012:
                   2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
  in studies
• Antibodies are a major source of experimental variability:
  –Same antibody can give very different results
  –Different antibodies to the same protein can give very
    different results
• Neuroscientists spend a lot of time tracking down
  antibodies and troubleshooting experiments that use
  antibodies, e.g.:




                                                                 Maryann Martone, Jan 2012:
                   2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
  in studies
• Antibodies are a major source of experimental variability:
  –Same antibody can give very different results
  –Different antibodies to the same protein can give very
    different results
• Neuroscientists spend a lot of time tracking down
  antibodies and troubleshooting experiments that use
  antibodies, e.g.:
Tissue sections were blocked with 5% serum and incubated
overnight at 4 °C with the following primary antibodies: anti-ChAT
(1:100; Millipore, Billerica, MA), anti-Bax (1:50; Santa Cruz), anti-
Bcl-xl (1:50; Cell Signaling), anti- neurofilament 200 kDa (1:200;
Millipore) ...
                                                                  Maryann Martone, Jan 2012:
                    2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
What studies used my monoclonal mouse
   antibody against actin in humans?

• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
  from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
  Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
  Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
  mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
  Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
  1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
  Santa Cruz Biotechnology)…




                                                                        Maryann Martone, Jan 2012:
                          2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
What studies used my monoclonal mouse
   antibody against actin in humans?

                                                                                         Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
  from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
  Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
  Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
  mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
  Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
  1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
  Santa Cruz Biotechnology)…




                                                                        Maryann Martone, Jan 2012:
                          2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
What studies used my monoclonal mouse
   antibody against actin in humans?

                                                                                         Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
  from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies antibody
                                 mAb=monoclonal
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
  Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
  Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
  mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
  Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
  1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
  Santa Cruz Biotechnology)…




                                                                        Maryann Martone, Jan 2012:
                          2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
What studies used my monoclonal mouse
   antibody against actin in humans?

                                                                                         Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
  from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies antibody
                                 mAb=monoclonal
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
  Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
  Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
  mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
  Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
  1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
  Santa Cruz Biotechnology)…




                                                                        Maryann Martone, Jan 2012:
                          2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
What studies used my monoclonal mouse
   antibody against actin in humans?

                                                                                         Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
  from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies antibody
                                 mAb=monoclonal
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
  Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
  Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
  mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
  Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
  1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
  Santa Cruz Biotechnology)…
                         •95 antibodies were identified in 8 articles
                         •52 did not contain enough information to
                         determine the antibody used



                                                                        Maryann Martone, Jan 2012:
                          2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
Problem #3: Knowledge is not
 connected to the real world




                          13
Problem #3: Knowledge is not
     connected to the real world
• No way to ensure connections between
  experiments/real-world manipulations and record




                                          13
Problem #3: Knowledge is not
     connected to the real world
• No way to ensure connections between
  experiments/real-world manipulations and record
• Specific characteristics of real-world objects
  matter, a great deal (e.g. patients, genes, etc)




                                           13
Problem #3: Knowledge is not
     connected to the real world
• No way to ensure connections between
  experiments/real-world manipulations and record
• Specific characteristics of real-world objects
  matter, a great deal (e.g. patients, genes, etc)
• Sometimes essential details are lost in statistical
  manipulation, pulling out certain features, etc.




                                              13
So what is missing?




                      14
So what is missing?
1.Lexapro example: we need to be able to trace claims
  throughout the evidence base




                                                 14
So what is missing?
1.Lexapro example: we need to be able to trace claims
  throughout the evidence base


2.Drug-drug interaction example: we need to access
  actionable content from papers




                                                 14
So what is missing?
1.Lexapro example: we need to be able to trace claims
  throughout the evidence base


2.Drug-drug interaction example: we need to access
  actionable content from papers


3.Antibodies example: we need to know which real-world
  objects the experiment was done on


                                                 14
So what is missing?
1.Lexapro example: we need to be able to trace claims
  throughout the evidence base

             Knowledge context
2.Drug-drug interaction example: we need to access
  actionable content from papers


3.Antibodies example: we need to know which real-world
  objects the experiment was done on


                                                 14
So what is missing?
1.Lexapro example: we need to be able to trace claims
  throughout the evidence base

             Knowledge context
2.Drug-drug interaction example: we need to access
  actionable content from papers
             Research context
3.Antibodies example: we need to know which real-world
  objects the experiment was done on


                                                 14
So what is missing?
1.Lexapro example: we need to be able to trace claims
  throughout the evidence base

             Knowledge context
2.Drug-drug interaction example: we need to access
  actionable content from papers
             Research context
3.Antibodies example: we need to know which real-world
  objects the experiment was done on

             Real-World context
                                                 14
Some projects addressing this:




                            15
Some projects addressing this:
• Knowledge context: manually trace and link
  evidence:
 –Data2Semantics: trace evidence for clinical guidelines
 –DIKB: trace heritage of Product Insert information




                                                  15
Some projects addressing this:
• Knowledge context: manually trace and link
  evidence:
 –Data2Semantics: trace evidence for clinical guidelines
 –DIKB: trace heritage of Product Insert information
• Experimental context: share workflow
  representations:
 –Workflow4Ever: share workflows
 –Yolanda Gil’s workflow design: share abstract workflows




                                                  15
Some projects addressing this:
• Knowledge context: manually trace and link
  evidence:
 –Data2Semantics: trace evidence for clinical guidelines
 –DIKB: trace heritage of Product Insert information
• Experimental context: share workflow
  representations:
 –Workflow4Ever: share workflows
 –Yolanda Gil’s workflow design: share abstract workflows
• Real-world context: manually look up the entities
 –NIF Antibodies registry
 –Pharmapendium
                                                  15
BUT:




       16
BUT:
• This is all manual: doesn’t scale
• It is all done after the data is already buried
• Papers act as if they are independent entities:
  we are not using the social, semantic web!




• Problem: the myth of the standalone article.
                                              16
What do we need?




[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
What do we need?




[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
What do we need?
  Internet of things: (Bleecker, [1])
  Interact with ‘objects that blog’ or ‘Blogjects’, that:
  track where they are and where they’ve been;
  have histories of their encounters and experiences
  have agency - an assertive voice on the social web [2]




[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
What do we need?
  Internet of things: (Bleecker, [1])
  Interact with ‘objects that blog’ or ‘Blogjects’, that:
  track where they are and where they’ve been;
  have histories of their encounters and experiences
  have agency - an assertive voice on the social web [2]




[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
What do we need?
  Internet of things: (Bleecker, [1])
  Interact with ‘objects that blog’ or ‘Blogjects’, that:
  track where they are and where they’ve been;
  have histories of their encounters and experiences
  have agency - an assertive voice on the social web [2]
  Research Objects: (Bechofer et al, [2])
  Create semantically rich aggregations of resources,
  that can possess some scientific intent or support
  some research objective




[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
What do we need?
  Internet of things: (Bleecker, [1])
  Interact with ‘objects that blog’ or ‘Blogjects’, that:
  track where they are and where they’ve been;
  have histories of their encounters and experiences
  have agency - an assertive voice on the social web [2]
  Research Objects: (Bechofer et al, [2])
  Create semantically rich aggregations of resources,
  that can possess some scientific intent or support
  some research objective




[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
What do we need?
  Internet of things: (Bleecker, [1])
  Interact with ‘objects that blog’ or ‘Blogjects’, that:
  track where they are and where they’ve been;
  have histories of their encounters and experiences
  have agency - an assertive voice on the social web [2]
  Research Objects: (Bechofer et al, [2])
  Create semantically rich aggregations of resources,
  that can possess some scientific intent or support
  some research objective
  Networked Knowledge: (Neylon, [3])
  If we care about taking advantage of the web and
  internet for research then we must tackle the building
  of scholarly communication networks.
  These networks will have two critical characteristics:
  scale and a lack of friction. [3]

[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
                                                                                                                       17
network-enabled-research/
Towards networked knowledge:




                         18
Towards networked knowledge:
Real-World context:




                           18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences




                                                 18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools




                                               18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:




                                               18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:




                                               18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
  describe our actions and observations




                                               18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
  describe our actions and observations
• Repositories for Research Objects with unique IDs,
  provenance, persistance



                                               18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
  describe our actions and observations
• Repositories for Research Objects with unique IDs,
  provenance, persistance
• Infrastructure to connect all of this, traverse it

                                               18
Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
  interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
  describe our actions and observations
• Repositories for Research Objects with unique IDs,
  provenance, persistance
• Infrastructure to connect all of this, traverse it
• Meta-analyses and visualisations to make sense of it.
                                               18
19
Knowledge context?




                     19
Knowledge context?
• We need:
 – A way to represent ‘Research Thoughts’: Observational and
   Interpretational assertions
 – To link them together in a meaningfully, approaching the richness
   of natural language
 – Tools to comment on other people’s (networked) thoughts, vet
   them, judge them, contradict/confirm
 – Interfaces to link knowledge back and forth through time/
   argument and oversee arguments




                                                            19
Knowledge context?
• We need:
 – A way to represent ‘Research Thoughts’: Observational and
   Interpretational assertions
 – To link them together in a meaningfully, approaching the richness
   of natural language
 – Tools to comment on other people’s (networked) thoughts, vet
   them, judge them, contradict/confirm
 – Interfaces to link knowledge back and forth through time/
   argument and oversee arguments
• We have:
 – Some interfaces
 – Some tools to pull out assertions




                                                            19
Knowledge context?
• We need:
    – A way to represent ‘Research Thoughts’: Observational and
      Interpretational assertions
    – To link them together in a meaningfully, approaching the richness
      of natural language
    – Tools to comment on other people’s (networked) thoughts, vet
      them, judge them, contradict/confirm
    – Interfaces to link knowledge back and forth through time/
      argument and oversee arguments
• We have:
    – Some interfaces
    – Some tools to pull out assertions
•


                                                               19
DOMEO: Annotating claims




                       20
DOMEO: Annotating claims




                       20
DOMEO: Annotating claims




                       20
DOMEO: Annotating claims




                       20
DOMEO: Annotating claims




                       20
Finding ‘Claimed Knowledge Updates’




                               21
Knowledge context - we need
       representation of text!
• Some tools exist, but...
• We need a) models and b) tools that create ROs and
  graft a structured narrative on top of that
• Containing the richness of models, thoughts,
  serendipity, associations...
• Mostly: we need reasons for people to do this and
  rewards for them to do so
• Some examples of enforced structure: grant proposals,
  data plans


                                                 22
Summary:




           23
Summary:
• Current way of publishing does not suffice!




                                                23
Summary:
• Current way of publishing does not suffice!
• We need networked knowledge to provide:
  –Real-world context
  –Experimental context
  –Knowledge context




                                                23
Summary:
• Current way of publishing does not suffice!
• We need networked knowledge to provide:
  –Real-world context
  –Experimental context
  –Knowledge context
• What do we have:
  –Real-World/Experimental: technologies, but no practice
  –Knowledge: some tools to encode this manually and
   help pull out semi-automatically




                                                      23
Summary:
• Current way of publishing does not suffice!
• We need networked knowledge to provide:
  –Real-world context
  –Experimental context
  –Knowledge context
• What do we have:
  –Real-World/Experimental: technologies, but no practice
  –Knowledge: some tools to encode this manually and
   help pull out semi-automatically
• What do we need:
  –A framework to tie this all together
  –Understanding of the authors’ drivers and rewards, to
   change their writing habits                          23
Some ideas for next steps:




                         24
Some ideas for next steps:
• Gathering of minds: Force11.org,
  BeyondThePDF, ScienceOnline, ...




                                     24
Some ideas for next steps:
• Gathering of minds: Force11.org,
  BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
  no real driver to collaborate




                                          24
Some ideas for next steps:
• Gathering of minds: Force11.org,
  BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
  no real driver to collaborate
• Need to develop use cases, for instance: drug-
  drug interaction experiments?




                                          24
Some ideas for next steps:
• Gathering of minds: Force11.org,
  BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
  no real driver to collaborate
• Need to develop use cases, for instance: drug-
  drug interaction experiments?
• Very interested to work on this: happy to
  discuss!



                                          24
Some ideas for next steps:
• Gathering of minds: Force11.org,
  BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
  no real driver to collaborate
• Need to develop use cases, for instance: drug-
  drug interaction experiments?
• Very interested to work on this: happy to
  discuss!


             a.dewaard@elsevier.com
                                          24
Acknowledgements:
• Collaborations:
 –DOMEO: Paolo Ciccarese, Tim Clark, Harvard
 –Data2Semantics: Rinke Hoekstra, Paul Groth, VU
 –DIKB: Rich Boycer, Jodi Schneider, Maria Liakata
 –Provenance and experimental modeling:
  Gully Burns, Eduard Hovy, Yolanda Gil, ISI
 –Linked Data Integration: Joanne Luciano, Deborah
  McGuiness, John Erickson, RPI
 –Claimed Knowledge Updates: Agnes Sandor, Xerox
• Discussions:
 –Phil Bourne, Cameron Neylon, Dave De Roure,
  Carole Goble, Brad Allen, Maryann Martone,
  Sophia Ananiadou                           25

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Cshals2012dewaardsmall

  • 1. Executing Semantics: Towards Networked Science Anita de Waard Disruptive Technologies Director Elsevier Labs, Jericho, VT
  • 2. Three problems, some solutions, and ideas for the future 2
  • 3. Three problems, some solutions, and ideas for the future • Three examples where scientific discourse falls short: why we need more context 2
  • 4. Three problems, some solutions, and ideas for the future • Three examples where scientific discourse falls short: why we need more context • Some projects that are modeling context now 2
  • 5. Three problems, some solutions, and ideas for the future • Three examples where scientific discourse falls short: why we need more context • Some projects that are modeling context now • What we really need: networked knowledge 2
  • 6. 1. Lexapro for adolescents 3
  • 7. 1. Lexapro for adolescents 3
  • 8. 1. Lexapro for adolescents 3
  • 9. 1. Lexapro for adolescents 3
  • 10. 4
  • 11. 4
  • 12. 5
  • 13. 5
  • 14. 5
  • 15. 5
  • 16. 6
  • 17. 6
  • 18. 6
  • 19. 6
  • 20. Problem #1: Knowledge is not connected or tracable 7
  • 21. Problem #1: Knowledge is not connected or tracable • How can we scale up the 1-to-1 interactions on efficacy and side effects happening today? 7
  • 22. Problem #1: Knowledge is not connected or tracable • How can we scale up the 1-to-1 interactions on efficacy and side effects happening today? • How do we know who is speaking in a patient forum? 7
  • 23. Problem #1: Knowledge is not connected or tracable • How can we scale up the 1-to-1 interactions on efficacy and side effects happening today? • How do we know who is speaking in a patient forum? • How to we get scientific knowledge in on this? 7
  • 24. Problem #1: Knowledge is not connected or tracable • How can we scale up the 1-to-1 interactions on efficacy and side effects happening today? • How do we know who is speaking in a patient forum? • How to we get scientific knowledge in on this? • How do we know who paid for knowledge? 7
  • 25. Problem #1: Knowledge is not connected or tracable • How can we scale up the 1-to-1 interactions on efficacy and side effects happening today? • How do we know who is speaking in a patient forum? • How to we get scientific knowledge in on this? • How do we know who paid for knowledge? • If a study is sponsored, how do you refer back to sources that link out to it? 7
  • 27. 2. Drug-drug interactions • Drug Interaction Knowledge Base 8
  • 28. 2. Drug-drug interactions • Drug Interaction Knowledge Base • Problem: how to integrate knowledge from various repositories and data stores into a single source 8
  • 29. 2. Drug-drug interactions • Drug Interaction Knowledge Base • Problem: how to integrate knowledge from various repositories and data stores into a single source • One of the main stumbling blocks: the way we record experiments in prose: 8
  • 30. E.g. Moltke et al, 1999: 9
  • 31. E.g. Moltke et al, 1999: After normalization for hepatic abundance, relative contributions to net intrinsic clearance were 37% for CYP2C19, 28% for CYP2D6, and 35% for CYP3A4. All samples were of the CYP2D6 and CYP2C19 normal metabolizer phenotype based on prior in vitro phenotyping studies. Average relative in vivo abundances, equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM) S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM), CYP2D6 (Km = 29 µM), and CYP3A4 (Km = 588 µM). The potential inhibitory effect of the stereoisomers of CT, DCT, and DDCT on the activity of six human cytochromes was evaluated using index reactions and methods as follows (Table 1): CYP1A2, phenacetin (100 µM) to acetaminophen (von Moltke et al.,1996a; Venkatakrishnan et al., 1998b); CYP2C9, tolbutamide (100 µM) to hydroxytolbutamide 9 (Venkatakrishnan et al., 1998c); CYP2C19, S-mephenytoin (25 µM) to 4′-OH-mephenytoin
  • 32. Problem #2: Knowledge is not actionable 10
  • 33. Problem #2: Knowledge is not actionable • Self-reference: 10
  • 34. Problem #2: Knowledge is not actionable • Self-reference: 10
  • 35. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. 10
  • 36. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: 10
  • 37. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: 10
  • 38. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). 10
  • 39. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). 10
  • 40. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). • Ways of describing meant for human eyes 10
  • 41. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). • Ways of describing meant for human eyes Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM) 10
  • 42. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). • Ways of describing meant for human eyes Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM) 10
  • 43. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). • Ways of describing meant for human eyes Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM) 10
  • 44. Problem #2: Knowledge is not actionable • Self-reference: R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers. • Reference to external data sources: Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000). • Ways of describing meant for human eyes Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM) • Many statements wrapped into one: S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM), CYP2D6 (Km = 29 µM), and CYP3A4 (Km = 588 µM). 10
  • 45. 3. NIF Antibody Study Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 46. 3. NIF Antibody Study • Pilot project to use text mining to identify antibodies used in studies Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 47. 3. NIF Antibody Study • Pilot project to use text mining to identify antibodies used in studies • Antibodies are a major source of experimental variability: –Same antibody can give very different results –Different antibodies to the same protein can give very different results Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 48. 3. NIF Antibody Study • Pilot project to use text mining to identify antibodies used in studies • Antibodies are a major source of experimental variability: –Same antibody can give very different results –Different antibodies to the same protein can give very different results • Neuroscientists spend a lot of time tracking down antibodies and troubleshooting experiments that use antibodies, e.g.: Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 49. 3. NIF Antibody Study • Pilot project to use text mining to identify antibodies used in studies • Antibodies are a major source of experimental variability: –Same antibody can give very different results –Different antibodies to the same protein can give very different results • Neuroscientists spend a lot of time tracking down antibodies and troubleshooting experiments that use antibodies, e.g.: Tissue sections were blocked with 5% serum and incubated overnight at 4 °C with the following primary antibodies: anti-ChAT (1:100; Millipore, Billerica, MA), anti-Bax (1:50; Santa Cruz), anti- Bcl-xl (1:50; Cell Signaling), anti- neurofilament 200 kDa (1:200; Millipore) ... Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 50. What studies used my monoclonal mouse antibody against actin in humans? • Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program • Immunoblot analysis and antibodies • The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 51. What studies used my monoclonal mouse antibody against actin in humans? Subject is Human • Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program • Immunoblot analysis and antibodies • The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 52. What studies used my monoclonal mouse antibody against actin in humans? Subject is Human • Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program • Immunoblot analysis and antibodies antibody mAb=monoclonal • The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 53. What studies used my monoclonal mouse antibody against actin in humans? Subject is Human • Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program • Immunoblot analysis and antibodies antibody mAb=monoclonal • The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 54. What studies used my monoclonal mouse antibody against actin in humans? Subject is Human • Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program • Immunoblot analysis and antibodies antibody mAb=monoclonal • The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… •95 antibodies were identified in 8 articles •52 did not contain enough information to determine the antibody used Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
  • 55. Problem #3: Knowledge is not connected to the real world 13
  • 56. Problem #3: Knowledge is not connected to the real world • No way to ensure connections between experiments/real-world manipulations and record 13
  • 57. Problem #3: Knowledge is not connected to the real world • No way to ensure connections between experiments/real-world manipulations and record • Specific characteristics of real-world objects matter, a great deal (e.g. patients, genes, etc) 13
  • 58. Problem #3: Knowledge is not connected to the real world • No way to ensure connections between experiments/real-world manipulations and record • Specific characteristics of real-world objects matter, a great deal (e.g. patients, genes, etc) • Sometimes essential details are lost in statistical manipulation, pulling out certain features, etc. 13
  • 59. So what is missing? 14
  • 60. So what is missing? 1.Lexapro example: we need to be able to trace claims throughout the evidence base 14
  • 61. So what is missing? 1.Lexapro example: we need to be able to trace claims throughout the evidence base 2.Drug-drug interaction example: we need to access actionable content from papers 14
  • 62. So what is missing? 1.Lexapro example: we need to be able to trace claims throughout the evidence base 2.Drug-drug interaction example: we need to access actionable content from papers 3.Antibodies example: we need to know which real-world objects the experiment was done on 14
  • 63. So what is missing? 1.Lexapro example: we need to be able to trace claims throughout the evidence base Knowledge context 2.Drug-drug interaction example: we need to access actionable content from papers 3.Antibodies example: we need to know which real-world objects the experiment was done on 14
  • 64. So what is missing? 1.Lexapro example: we need to be able to trace claims throughout the evidence base Knowledge context 2.Drug-drug interaction example: we need to access actionable content from papers Research context 3.Antibodies example: we need to know which real-world objects the experiment was done on 14
  • 65. So what is missing? 1.Lexapro example: we need to be able to trace claims throughout the evidence base Knowledge context 2.Drug-drug interaction example: we need to access actionable content from papers Research context 3.Antibodies example: we need to know which real-world objects the experiment was done on Real-World context 14
  • 67. Some projects addressing this: • Knowledge context: manually trace and link evidence: –Data2Semantics: trace evidence for clinical guidelines –DIKB: trace heritage of Product Insert information 15
  • 68. Some projects addressing this: • Knowledge context: manually trace and link evidence: –Data2Semantics: trace evidence for clinical guidelines –DIKB: trace heritage of Product Insert information • Experimental context: share workflow representations: –Workflow4Ever: share workflows –Yolanda Gil’s workflow design: share abstract workflows 15
  • 69. Some projects addressing this: • Knowledge context: manually trace and link evidence: –Data2Semantics: trace evidence for clinical guidelines –DIKB: trace heritage of Product Insert information • Experimental context: share workflow representations: –Workflow4Ever: share workflows –Yolanda Gil’s workflow design: share abstract workflows • Real-world context: manually look up the entities –NIF Antibodies registry –Pharmapendium 15
  • 70. BUT: 16
  • 71. BUT: • This is all manual: doesn’t scale • It is all done after the data is already buried • Papers act as if they are independent entities: we are not using the social, semantic web! • Problem: the myth of the standalone article. 16
  • 72. What do we need? [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 73. What do we need? [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 74. What do we need? Internet of things: (Bleecker, [1]) Interact with ‘objects that blog’ or ‘Blogjects’, that: track where they are and where they’ve been; have histories of their encounters and experiences have agency - an assertive voice on the social web [2] [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 75. What do we need? Internet of things: (Bleecker, [1]) Interact with ‘objects that blog’ or ‘Blogjects’, that: track where they are and where they’ve been; have histories of their encounters and experiences have agency - an assertive voice on the social web [2] [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 76. What do we need? Internet of things: (Bleecker, [1]) Interact with ‘objects that blog’ or ‘Blogjects’, that: track where they are and where they’ve been; have histories of their encounters and experiences have agency - an assertive voice on the social web [2] Research Objects: (Bechofer et al, [2]) Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 77. What do we need? Internet of things: (Bleecker, [1]) Interact with ‘objects that blog’ or ‘Blogjects’, that: track where they are and where they’ve been; have histories of their encounters and experiences have agency - an assertive voice on the social web [2] Research Objects: (Bechofer et al, [2]) Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 78. What do we need? Internet of things: (Bleecker, [1]) Interact with ‘objects that blog’ or ‘Blogjects’, that: track where they are and where they’ve been; have histories of their encounters and experiences have agency - an assertive voice on the social web [2] Research Objects: (Bechofer et al, [2]) Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective Networked Knowledge: (Neylon, [3]) If we care about taking advantage of the web and internet for research then we must tackle the building of scholarly communication networks. These networks will have two critical characteristics: scale and a lack of friction. [3] [[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/ 17 network-enabled-research/
  • 81. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences 18
  • 82. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools 18
  • 83. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools Research context: 18
  • 84. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools Research context: 18
  • 85. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools Research context: • Tools to create Research Objects and allow us to describe our actions and observations 18
  • 86. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools Research context: • Tools to create Research Objects and allow us to describe our actions and observations • Repositories for Research Objects with unique IDs, provenance, persistance 18
  • 87. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools Research context: • Tools to create Research Objects and allow us to describe our actions and observations • Repositories for Research Objects with unique IDs, provenance, persistance • Infrastructure to connect all of this, traverse it 18
  • 88. Towards networked knowledge: Real-World context: • Networked Objects in the lab that store our interactions with them and their experiences • Easy ways to author our experiences with these tools Research context: • Tools to create Research Objects and allow us to describe our actions and observations • Repositories for Research Objects with unique IDs, provenance, persistance • Infrastructure to connect all of this, traverse it • Meta-analyses and visualisations to make sense of it. 18
  • 89. 19
  • 91. Knowledge context? • We need: – A way to represent ‘Research Thoughts’: Observational and Interpretational assertions – To link them together in a meaningfully, approaching the richness of natural language – Tools to comment on other people’s (networked) thoughts, vet them, judge them, contradict/confirm – Interfaces to link knowledge back and forth through time/ argument and oversee arguments 19
  • 92. Knowledge context? • We need: – A way to represent ‘Research Thoughts’: Observational and Interpretational assertions – To link them together in a meaningfully, approaching the richness of natural language – Tools to comment on other people’s (networked) thoughts, vet them, judge them, contradict/confirm – Interfaces to link knowledge back and forth through time/ argument and oversee arguments • We have: – Some interfaces – Some tools to pull out assertions 19
  • 93. Knowledge context? • We need: – A way to represent ‘Research Thoughts’: Observational and Interpretational assertions – To link them together in a meaningfully, approaching the richness of natural language – Tools to comment on other people’s (networked) thoughts, vet them, judge them, contradict/confirm – Interfaces to link knowledge back and forth through time/ argument and oversee arguments • We have: – Some interfaces – Some tools to pull out assertions • 19
  • 100. Knowledge context - we need representation of text! • Some tools exist, but... • We need a) models and b) tools that create ROs and graft a structured narrative on top of that • Containing the richness of models, thoughts, serendipity, associations... • Mostly: we need reasons for people to do this and rewards for them to do so • Some examples of enforced structure: grant proposals, data plans 22
  • 101. Summary: 23
  • 102. Summary: • Current way of publishing does not suffice! 23
  • 103. Summary: • Current way of publishing does not suffice! • We need networked knowledge to provide: –Real-world context –Experimental context –Knowledge context 23
  • 104. Summary: • Current way of publishing does not suffice! • We need networked knowledge to provide: –Real-world context –Experimental context –Knowledge context • What do we have: –Real-World/Experimental: technologies, but no practice –Knowledge: some tools to encode this manually and help pull out semi-automatically 23
  • 105. Summary: • Current way of publishing does not suffice! • We need networked knowledge to provide: –Real-world context –Experimental context –Knowledge context • What do we have: –Real-World/Experimental: technologies, but no practice –Knowledge: some tools to encode this manually and help pull out semi-automatically • What do we need: –A framework to tie this all together –Understanding of the authors’ drivers and rewards, to change their writing habits 23
  • 106. Some ideas for next steps: 24
  • 107. Some ideas for next steps: • Gathering of minds: Force11.org, BeyondThePDF, ScienceOnline, ... 24
  • 108. Some ideas for next steps: • Gathering of minds: Force11.org, BeyondThePDF, ScienceOnline, ... • People and ideas are converging but there is no real driver to collaborate 24
  • 109. Some ideas for next steps: • Gathering of minds: Force11.org, BeyondThePDF, ScienceOnline, ... • People and ideas are converging but there is no real driver to collaborate • Need to develop use cases, for instance: drug- drug interaction experiments? 24
  • 110. Some ideas for next steps: • Gathering of minds: Force11.org, BeyondThePDF, ScienceOnline, ... • People and ideas are converging but there is no real driver to collaborate • Need to develop use cases, for instance: drug- drug interaction experiments? • Very interested to work on this: happy to discuss! 24
  • 111. Some ideas for next steps: • Gathering of minds: Force11.org, BeyondThePDF, ScienceOnline, ... • People and ideas are converging but there is no real driver to collaborate • Need to develop use cases, for instance: drug- drug interaction experiments? • Very interested to work on this: happy to discuss! a.dewaard@elsevier.com 24
  • 112. Acknowledgements: • Collaborations: –DOMEO: Paolo Ciccarese, Tim Clark, Harvard –Data2Semantics: Rinke Hoekstra, Paul Groth, VU –DIKB: Rich Boycer, Jodi Schneider, Maria Liakata –Provenance and experimental modeling: Gully Burns, Eduard Hovy, Yolanda Gil, ISI –Linked Data Integration: Joanne Luciano, Deborah McGuiness, John Erickson, RPI –Claimed Knowledge Updates: Agnes Sandor, Xerox • Discussions: –Phil Bourne, Cameron Neylon, Dave De Roure, Carole Goble, Brad Allen, Maryann Martone, Sophia Ananiadou 25