SlideShare une entreprise Scribd logo
1  sur  94
The Seven Deadly Sins of Bioinformatics Professor Carole Goble [email_address] The University of Manchester, UK The myGrid project OMII-UK
Roadmap ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Intractable Problems in Bioinformatics. Have we sinned? Are these part of the intractable problem?
The traditional sins…. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],http://en.wikipedia.org/wiki/Seven_deadly_sins [Stevens and Lord]
Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
I am grateful to… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
They came up with more than seven. But I beat them into submission. Many are highly inter-related. Hopefully they are all too familiar.
Sins ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Sin 1
Reinvention ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Comparative Genomics? Tisk! Its Comparative Bioinformatics Bioinformatics is about mapping one schema to another, one format to another, one id scheme to another. What a waste of time.  What a handy distraction from doing some Real Science™.
Names and Identity Crisis Q92983 O00275 O00276 O00277 O00278 O00279 O00280 O14865 O14866 P78507 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Q93038 = Tumor necrosis factor receptor superfamily member 25 precursor  P78515 Q93036  Q93037  Q99722  Q99830  Q99831  Q9BY86  Q9UME0  Q9UME1  Q9UME5 Annotation history:  http://www.expasy.org/uniprot/Q93038
Andy Law's Third Law ,[object Object],http://bioinformatics.roslin.ac.uk/lawslaws.html
The Selfish Scientist ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some causes of the Identity Crisis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Pocock]
Id Reinvention ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],urn:lsid:uniprot.org:{db}:{id}     http:// purl.uniprot.org /{db }/{id}
Andy Law’s First (Format) Law ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],http://bioinformatics.roslin.ac.uk/lawslaws.html
[object Object],[object Object],[object Object]
Reinvention of Ontology tools ,[object Object],[object Object],The Montagues and The Capulets.. Let me get my bullet-proof vest …
The “Oh No” OBO Pragmatists Aesthetics Philosophers Life  Scientists Capulets Knowledge Representation Montagues A means to an end Content providers Theoreticians The end Mechanism providers Spiritual guides The Montagues and The Capulets …SOFG 2004, KCap 2005, Comparative and Functional Genomics  2004 Endurants, Perdurants, Being, Substance, Event
Yet another database … ,[object Object],[object Object],[object Object],FlyBase, WormBase, SGD, BeeBase and many other large and small community databases
BioBabel ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Integration ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
Any more ? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Reuse Rocks. Collaboration through  workflow and web services ,[object Object],[object Object],[object Object],[object Object]
Recycling, Reuse, Repurposing ,[object Object],[object Object],[object Object]
Warning! Reuse is Hard ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bullying and the Borg ,[object Object],[object Object],[object Object],[object Object]
Reinvention or Invention? Pre-dating ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A few months in the laboratory (or the computer) can save a few hours in the library (or on Google). Westheimer's Law (with additions).
No tool is an island… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
I know what it means... ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],“ AI limericks” by Henry Kautz http:// www.cs.washington.edu/homes/kautz/misc/limericks.html
Not just bioinformatics  Computer Science is Guilty!
Why don’t biologists modularise OWL ontologies properly? Er, well, like how should we do it “properly” and where are the tools to help us? We don’t know and we haven’t got any. But here are some vague guidelines.  W3C Semantic Web for Life Sciences mailing list, 2005
“ I don't blame them [MGED/PSI community] because to truly comprehend RDF/OWL is not an easy task, it takes not just the understand of technology itself but more so the vision on how things should and can work in SW.” “ One thing we have to remember is that biologists are building ontologies to do a job of work. They are not produced as some end of CS or SW research” “ Principles are all well and good, but we should know from decades of software engineering that saying "do it properly" isn't a solution. We need tooling and methodologies that do not in themselves hinder a domain specialist. In many cases it is easier to re-develop than re-use or even cut-and-paste from an existing ontology than it is to muck around “doing it properly”” “ There is actually a gap between the view of ontology for CS people and for biological people. The ontology in biologist's eyes are more of a treaty than logical representation, that in CS view is on the reverse of that view. It needs dialog to bring the view to a middle ground and mechanisms to stretch to both directions.”
Standards are boring (but important) ,[object Object],[object Object],[object Object],[object Object]
Self promotion ,[object Object],[object Object],[object Object],[object Object],Not all software and databases are equal.
Research – Production Confusion ,[object Object],[object Object],[object Object],[object Object]
Trust I don’t trust your code I don’t trust your data I don’t trust you will still be around in 1 year
Sin 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Biologist exceptionalism ,[object Object],[object Object],I’m different. We are all individuals.
Biological exceptionalism ,[object Object],[object Object],[object Object],[object Object],[object Object]
We are so much more complex… ,[object Object],[object Object],[object Object]
Other Sciences…. ,[object Object],[object Object],[object Object],[object Object]
Biology Exceptionalism ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sin 3 ,[object Object],[object Object],[object Object],[object Object]
Autonomy is death! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Lincoln Stein said a while ago… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],… and he could say it again today.
Law's Second Law ,[object Object]
Workflow commodities ,[object Object],[object Object],[object Object],[object Object],[object Object]
The myGrid Semantic Sweatshop ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Semantic
The myGrid Semantic Sweatshop  notice how tired they look Franck Tanoh Katy Wolstencroft
Churn, Churn, Churn ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Churn, Churn, Churn ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sin 4 ,[object Object],[object Object],[object Object],[object Object],[object Object]
I know it all. ,[object Object],[object Object],[object Object],[object Object],[object Object],And what would you suggest, Mr. Smartie Pants?
Think like me!  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Misunderstanding and disrespecting users
A good User Experience outweighs smart features. Can I use it?  Is the user interface familiar? Does it fit with my needs?
Gain-Pain pay-off ,[object Object],Gain Pain Very BAD Good, but Unlikely Just right
Sin 5 ,[object Object],[object Object],[object Object],[object Object]
More, more, more! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Cameron]
The trouble with warehouses ,[object Object],[object Object],[object Object],[object Object],[object Object]
More More More  ,[object Object],[object Object],[object Object],[object Object]
Mash-Up Data Marshalling ,[object Object],[object Object],[object Object],[object Object],Mash Up Application User interface Protocol objects Protocol Protocol
Distributed Annotation System Mash-Up  http://www.biodas.org Reference Server AC003027 AC005122 M10154 Annotation Server Annotation Server AC003027 M10154 WI1029 AFM820 AFM1126 WI443 AC005122 Annotation Server
Sin 6 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Ennui ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Its black and white ,[object Object],[object Object],[object Object],[object Object],[object Object]
Quality Delusions ,[object Object],[object Object],[object Object],[object Object]
Quality Delusions ,[object Object],[object Object],[object Object],[object Object]
Black Box Science ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ No experiment is reproducible.”  Wyszowski's Law “ An experiment is reproducible until another laboratory tries to repeat it.”  Alexander Kohn
Sin 7 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],www.CartoonStock.com  .
Hackery ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ I am sure one could reuse large parts of re-annotation for building transcriptome maps, if they only used workflows and ontologies”.   Marco Roos A Biologist and Bioinformatician VL-e Project, Amsterdam
“ Bioinformaticians have reached the standards of the 1980s, while computer scientists are working on the standards of the 2020s, leaving roughly 40 years to bridge.   Marco Roos A Biologist and Bioinformatician VL-e Project, Amsterdam
Blind faith in XML  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],XML
Blind Faith in Foo. ,[object Object],[object Object],[object Object],[object Object]
Pioneering development methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open Source Blinkers ,[object Object],[object Object],[object Object]
Sin Summary Maybe only one “original sin” in bioinformatics. Parochialism and Insularity Exceptionalism Autonomy or death! Vanity: Pride and Narcissism Monolith Meglomania   Scientific method Sloth Instant Gratification Reinvention Churn
Can we become less sinful?  Why do these sins exist? Are bioinformaticians particularly naughty? No naughtier than Computer Scientists. And its all very hard. Though they are naughty…
Why? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Luddism? Surely not! ,[object Object],[object Object],[object Object],[object Object],[Stevens]
Research – Production Confusion ,[object Object],[object Object],[object Object],[object Object]
Practical Steps? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FaceBook & Bazaar for  Workflow e-Scientists myexperiment.org Trials start  August 2007!
Delivery Bulge
Practical Steps for IT Platforms? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Practical Steps? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Web 2.0 Design Patterns ,[object Object],26/2/2007  |  myExperiment  |  Slide  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Practical Steps? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Final Word Sin writes histories, goodness is silent.     Thomas Fuller

Contenu connexe

Tendances

Big Data in Healthcare: Hype and Hope on the Path to Personalized Medicine
Big Data in Healthcare: Hype and Hope on the Path to Personalized MedicineBig Data in Healthcare: Hype and Hope on the Path to Personalized Medicine
Big Data in Healthcare: Hype and Hope on the Path to Personalized MedicineNew York eHealth Collaborative
 
Research Paper Abstract - Impact of AI on Employment
Research Paper Abstract - Impact of AI on EmploymentResearch Paper Abstract - Impact of AI on Employment
Research Paper Abstract - Impact of AI on EmploymentMrudul Manojkumar
 
GenAI in Research with Responsible AI
GenAI in Researchwith Responsible AIGenAI in Researchwith Responsible AI
GenAI in Research with Responsible AILiming Zhu
 
AI in Banking and Financial Services
AI in Banking and Financial ServicesAI in Banking and Financial Services
AI in Banking and Financial ServicesNiraj Vaidya
 
AI in Healthcare: Defining New Health
AI in Healthcare: Defining New HealthAI in Healthcare: Defining New Health
AI in Healthcare: Defining New HealthKumaraguru Veerasamy
 
AI in Healthcare | Future of Smart Hospitals
AI in Healthcare | Future of Smart Hospitals AI in Healthcare | Future of Smart Hospitals
AI in Healthcare | Future of Smart Hospitals Renee Yao
 
Explainability and bias in AI
Explainability and bias in AIExplainability and bias in AI
Explainability and bias in AIBill Liu
 
Healthcare Data Warehouse Models Explained
Healthcare Data Warehouse Models ExplainedHealthcare Data Warehouse Models Explained
Healthcare Data Warehouse Models ExplainedHealth Catalyst
 
A Privacy Framework for Hierarchical Federated Learning
A Privacy Framework for Hierarchical Federated LearningA Privacy Framework for Hierarchical Federated Learning
A Privacy Framework for Hierarchical Federated LearningDebmalya Biswas
 
The Analytics and Data Science Landscape
The Analytics and Data Science LandscapeThe Analytics and Data Science Landscape
The Analytics and Data Science LandscapePhilip Bourne
 
Predictive Analytics and Machine Learning for Healthcare - Diabetes
Predictive Analytics and Machine Learning for Healthcare - DiabetesPredictive Analytics and Machine Learning for Healthcare - Diabetes
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
 
Big data in healthcare
Big data in healthcareBig data in healthcare
Big data in healthcareDeZyre
 
How-to-Build-a-Career-in-AI.pdf
How-to-Build-a-Career-in-AI.pdfHow-to-Build-a-Career-in-AI.pdf
How-to-Build-a-Career-in-AI.pdfDustin Liu
 
Difference between Artificial Intelligence, Machine Learning, Deep Learning a...
Difference between Artificial Intelligence, Machine Learning, Deep Learning a...Difference between Artificial Intelligence, Machine Learning, Deep Learning a...
Difference between Artificial Intelligence, Machine Learning, Deep Learning a...Sanjay Srivastava
 
Big data and AI presentation slides
Big data and AI presentation slidesBig data and AI presentation slides
Big data and AI presentation slidesCloudxLab
 
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
 
Artificial Intelligence in Medicine and Healthcare
Artificial Intelligence in Medicine and HealthcareArtificial Intelligence in Medicine and Healthcare
Artificial Intelligence in Medicine and HealthcareAgnieszka Maria Walorska
 
Introduction to LLMs
Introduction to LLMsIntroduction to LLMs
Introduction to LLMsLoic Merckel
 

Tendances (20)

Big Data in Healthcare: Hype and Hope on the Path to Personalized Medicine
Big Data in Healthcare: Hype and Hope on the Path to Personalized MedicineBig Data in Healthcare: Hype and Hope on the Path to Personalized Medicine
Big Data in Healthcare: Hype and Hope on the Path to Personalized Medicine
 
Research Paper Abstract - Impact of AI on Employment
Research Paper Abstract - Impact of AI on EmploymentResearch Paper Abstract - Impact of AI on Employment
Research Paper Abstract - Impact of AI on Employment
 
GenAI in Research with Responsible AI
GenAI in Researchwith Responsible AIGenAI in Researchwith Responsible AI
GenAI in Research with Responsible AI
 
AI in Banking and Financial Services
AI in Banking and Financial ServicesAI in Banking and Financial Services
AI in Banking and Financial Services
 
AI in Healthcare: Defining New Health
AI in Healthcare: Defining New HealthAI in Healthcare: Defining New Health
AI in Healthcare: Defining New Health
 
AI in Healthcare | Future of Smart Hospitals
AI in Healthcare | Future of Smart Hospitals AI in Healthcare | Future of Smart Hospitals
AI in Healthcare | Future of Smart Hospitals
 
Explainability and bias in AI
Explainability and bias in AIExplainability and bias in AI
Explainability and bias in AI
 
Healthcare Data Warehouse Models Explained
Healthcare Data Warehouse Models ExplainedHealthcare Data Warehouse Models Explained
Healthcare Data Warehouse Models Explained
 
A Privacy Framework for Hierarchical Federated Learning
A Privacy Framework for Hierarchical Federated LearningA Privacy Framework for Hierarchical Federated Learning
A Privacy Framework for Hierarchical Federated Learning
 
Intro to LLMs
Intro to LLMsIntro to LLMs
Intro to LLMs
 
The Analytics and Data Science Landscape
The Analytics and Data Science LandscapeThe Analytics and Data Science Landscape
The Analytics and Data Science Landscape
 
Predictive Analytics and Machine Learning for Healthcare - Diabetes
Predictive Analytics and Machine Learning for Healthcare - DiabetesPredictive Analytics and Machine Learning for Healthcare - Diabetes
Predictive Analytics and Machine Learning for Healthcare - Diabetes
 
Big data in healthcare
Big data in healthcareBig data in healthcare
Big data in healthcare
 
How-to-Build-a-Career-in-AI.pdf
How-to-Build-a-Career-in-AI.pdfHow-to-Build-a-Career-in-AI.pdf
How-to-Build-a-Career-in-AI.pdf
 
Difference between Artificial Intelligence, Machine Learning, Deep Learning a...
Difference between Artificial Intelligence, Machine Learning, Deep Learning a...Difference between Artificial Intelligence, Machine Learning, Deep Learning a...
Difference between Artificial Intelligence, Machine Learning, Deep Learning a...
 
Big data and AI presentation slides
Big data and AI presentation slidesBig data and AI presentation slides
Big data and AI presentation slides
 
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...
 
Artificial Intelligence in Medicine and Healthcare
Artificial Intelligence in Medicine and HealthcareArtificial Intelligence in Medicine and Healthcare
Artificial Intelligence in Medicine and Healthcare
 
AI in the Covid-19 pandemic
AI in the Covid-19 pandemicAI in the Covid-19 pandemic
AI in the Covid-19 pandemic
 
Introduction to LLMs
Introduction to LLMsIntroduction to LLMs
Introduction to LLMs
 

En vedette

Sequence Alignment In Bioinformatics
Sequence Alignment In BioinformaticsSequence Alignment In Bioinformatics
Sequence Alignment In BioinformaticsNikesh Narayanan
 
Applications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessApplications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessProf. Dr. Basavaraj Nanjwade
 
Bioinformatics
BioinformaticsBioinformatics
BioinformaticsJTADrexel
 
Introduction to column oriented databases
Introduction to column oriented databasesIntroduction to column oriented databases
Introduction to column oriented databasesArangoDB Database
 
Application of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciencesApplication of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciencesSobia
 
Basics of bioinformatics
Basics of bioinformaticsBasics of bioinformatics
Basics of bioinformaticsAbhishek Vatsa
 
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksResults Vary: The Pragmatics of Reproducibility and Research Object Frameworks
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
 
Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015Surya Saha
 
Perl 5.10 for People Who Aren't Totally Insane
Perl 5.10 for People Who Aren't Totally InsanePerl 5.10 for People Who Aren't Totally Insane
Perl 5.10 for People Who Aren't Totally InsaneRicardo Signes
 
Asynchronous programming with AnyEvent
Asynchronous programming with AnyEventAsynchronous programming with AnyEvent
Asynchronous programming with AnyEventTatsuhiko Miyagawa
 
Usability and Bioinformatics: experience and research challenges
Usability and Bioinformatics: experience and research challengesUsability and Bioinformatics: experience and research challenges
Usability and Bioinformatics: experience and research challengesbolk
 
Integrative_omics_lecture_feb112016_UAB
Integrative_omics_lecture_feb112016_UABIntegrative_omics_lecture_feb112016_UAB
Integrative_omics_lecture_feb112016_UABSophia Banton
 
BPIPE: a bioinformatics pipeline framework
BPIPE: a bioinformatics pipeline frameworkBPIPE: a bioinformatics pipeline framework
BPIPE: a bioinformatics pipeline frameworkMohamed Nadhir Djekidel
 
Multi-omics Pathway Visualization
Multi-omics Pathway VisualizationMulti-omics Pathway Visualization
Multi-omics Pathway VisualizationAnwesha Bohler
 
Protein function and bioinformatics
Protein function and bioinformaticsProtein function and bioinformatics
Protein function and bioinformaticsNeil Saunders
 
The Ondex Data Integration Framework
The Ondex Data Integration FrameworkThe Ondex Data Integration Framework
The Ondex Data Integration Frameworkbosc
 

En vedette (20)

Sequence Alignment In Bioinformatics
Sequence Alignment In BioinformaticsSequence Alignment In Bioinformatics
Sequence Alignment In Bioinformatics
 
Applications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessApplications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And Process
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
Introduction to column oriented databases
Introduction to column oriented databasesIntroduction to column oriented databases
Introduction to column oriented databases
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
Bioinformatics principles and applications
Bioinformatics principles and applicationsBioinformatics principles and applications
Bioinformatics principles and applications
 
Application of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciencesApplication of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciences
 
Basics of bioinformatics
Basics of bioinformaticsBasics of bioinformatics
Basics of bioinformatics
 
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksResults Vary: The Pragmatics of Reproducibility and Research Object Frameworks
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
 
Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015
 
Perl 5.10 for People Who Aren't Totally Insane
Perl 5.10 for People Who Aren't Totally InsanePerl 5.10 for People Who Aren't Totally Insane
Perl 5.10 for People Who Aren't Totally Insane
 
Asynchronous programming with AnyEvent
Asynchronous programming with AnyEventAsynchronous programming with AnyEvent
Asynchronous programming with AnyEvent
 
Usability and Bioinformatics: experience and research challenges
Usability and Bioinformatics: experience and research challengesUsability and Bioinformatics: experience and research challenges
Usability and Bioinformatics: experience and research challenges
 
B4OS-2012
B4OS-2012B4OS-2012
B4OS-2012
 
Integrative_omics_lecture_feb112016_UAB
Integrative_omics_lecture_feb112016_UABIntegrative_omics_lecture_feb112016_UAB
Integrative_omics_lecture_feb112016_UAB
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
BPIPE: a bioinformatics pipeline framework
BPIPE: a bioinformatics pipeline frameworkBPIPE: a bioinformatics pipeline framework
BPIPE: a bioinformatics pipeline framework
 
Multi-omics Pathway Visualization
Multi-omics Pathway VisualizationMulti-omics Pathway Visualization
Multi-omics Pathway Visualization
 
Protein function and bioinformatics
Protein function and bioinformaticsProtein function and bioinformatics
Protein function and bioinformatics
 
The Ondex Data Integration Framework
The Ondex Data Integration FrameworkThe Ondex Data Integration Framework
The Ondex Data Integration Framework
 

Similaire à The Seven Deadly Sins of Bioinformatics

Data analysis & integration challenges in genomics
Data analysis & integration challenges in genomicsData analysis & integration challenges in genomics
Data analysis & integration challenges in genomicsmikaelhuss
 
Services For Science April 2009
Services For Science April 2009Services For Science April 2009
Services For Science April 2009Ian Foster
 
Web Science, SADI, and the Singularity
Web Science, SADI, and the SingularityWeb Science, SADI, and the Singularity
Web Science, SADI, and the SingularityMark Wilkinson
 
Introduction to Ontologies for Environmental Biology
Introduction to Ontologies for Environmental BiologyIntroduction to Ontologies for Environmental Biology
Introduction to Ontologies for Environmental BiologyBarry Smith
 
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Semantics for Bioinformatics: What, Why and How of Search, Integration and An...
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
 
wolstencroft-ogf20-astro
wolstencroft-ogf20-astrowolstencroft-ogf20-astro
wolstencroft-ogf20-astrowebuploader
 
Being Reproducible: SSBSS Summer School 2017
Being Reproducible: SSBSS Summer School 2017Being Reproducible: SSBSS Summer School 2017
Being Reproducible: SSBSS Summer School 2017Carole Goble
 
The Past, Present and Future of Knowledge in Biology
The Past, Present and Future of Knowledge in BiologyThe Past, Present and Future of Knowledge in Biology
The Past, Present and Future of Knowledge in Biologyrobertstevens65
 
Computing on the shoulders of giants
Computing on the shoulders of giantsComputing on the shoulders of giants
Computing on the shoulders of giantsBenjamin Good
 
Ontology - and Reloaded and Revolutions
Ontology - and Reloaded and RevolutionsOntology - and Reloaded and Revolutions
Ontology - and Reloaded and RevolutionsJie Bao
 
Web Science - ISoLA 2012
Web Science - ISoLA 2012Web Science - ISoLA 2012
Web Science - ISoLA 2012Mark Wilkinson
 
Can machines understand the scientific literature
Can machines understand the scientific literatureCan machines understand the scientific literature
Can machines understand the scientific literaturepetermurrayrust
 
Life Sciences De-Mystified - Mark Bünger - PICNIC '10
Life Sciences De-Mystified - Mark Bünger - PICNIC '10Life Sciences De-Mystified - Mark Bünger - PICNIC '10
Life Sciences De-Mystified - Mark Bünger - PICNIC '10PICNIC Festival
 
Dynamic Semantic Metadata in Biomedical Communications
Dynamic Semantic Metadata in Biomedical CommunicationsDynamic Semantic Metadata in Biomedical Communications
Dynamic Semantic Metadata in Biomedical CommunicationsTim Clark
 
Emerging Forms of Data and Analytics
Emerging Forms of Data and AnalyticsEmerging Forms of Data and Analytics
Emerging Forms of Data and AnalyticsDavid De Roure
 
download
downloaddownload
downloadbutest
 

Similaire à The Seven Deadly Sins of Bioinformatics (20)

A biologist in e-Science
A biologist in e-ScienceA biologist in e-Science
A biologist in e-Science
 
DCC Keynote 2007
DCC Keynote 2007DCC Keynote 2007
DCC Keynote 2007
 
Data analysis & integration challenges in genomics
Data analysis & integration challenges in genomicsData analysis & integration challenges in genomics
Data analysis & integration challenges in genomics
 
Services For Science April 2009
Services For Science April 2009Services For Science April 2009
Services For Science April 2009
 
Web Science, SADI, and the Singularity
Web Science, SADI, and the SingularityWeb Science, SADI, and the Singularity
Web Science, SADI, and the Singularity
 
Introduction to Ontologies for Environmental Biology
Introduction to Ontologies for Environmental BiologyIntroduction to Ontologies for Environmental Biology
Introduction to Ontologies for Environmental Biology
 
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Semantics for Bioinformatics: What, Why and How of Search, Integration and An...
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...
 
wolstencroft-ogf20-astro
wolstencroft-ogf20-astrowolstencroft-ogf20-astro
wolstencroft-ogf20-astro
 
Being Reproducible: SSBSS Summer School 2017
Being Reproducible: SSBSS Summer School 2017Being Reproducible: SSBSS Summer School 2017
Being Reproducible: SSBSS Summer School 2017
 
The Past, Present and Future of Knowledge in Biology
The Past, Present and Future of Knowledge in BiologyThe Past, Present and Future of Knowledge in Biology
The Past, Present and Future of Knowledge in Biology
 
Computing on the shoulders of giants
Computing on the shoulders of giantsComputing on the shoulders of giants
Computing on the shoulders of giants
 
Ontology - and Reloaded and Revolutions
Ontology - and Reloaded and RevolutionsOntology - and Reloaded and Revolutions
Ontology - and Reloaded and Revolutions
 
Web Science - ISoLA 2012
Web Science - ISoLA 2012Web Science - ISoLA 2012
Web Science - ISoLA 2012
 
Improving online chemistry one structure at a time
Improving online chemistry one structure at a timeImproving online chemistry one structure at a time
Improving online chemistry one structure at a time
 
Can machines understand the scientific literature
Can machines understand the scientific literatureCan machines understand the scientific literature
Can machines understand the scientific literature
 
Demo Presentation Wageningen Text Mining Workshop 2007
Demo Presentation Wageningen Text Mining Workshop 2007Demo Presentation Wageningen Text Mining Workshop 2007
Demo Presentation Wageningen Text Mining Workshop 2007
 
Life Sciences De-Mystified - Mark Bünger - PICNIC '10
Life Sciences De-Mystified - Mark Bünger - PICNIC '10Life Sciences De-Mystified - Mark Bünger - PICNIC '10
Life Sciences De-Mystified - Mark Bünger - PICNIC '10
 
Dynamic Semantic Metadata in Biomedical Communications
Dynamic Semantic Metadata in Biomedical CommunicationsDynamic Semantic Metadata in Biomedical Communications
Dynamic Semantic Metadata in Biomedical Communications
 
Emerging Forms of Data and Analytics
Emerging Forms of Data and AnalyticsEmerging Forms of Data and Analytics
Emerging Forms of Data and Analytics
 
download
downloaddownload
download
 

Plus de Duncan Hull

Why study plants?
Why study plants?Why study plants?
Why study plants?Duncan Hull
 
Embedding employability in the Computer Science curriculum
Embedding employability in the Computer Science curriculumEmbedding employability in the Computer Science curriculum
Embedding employability in the Computer Science curriculumDuncan Hull
 
Wikipedia at the Royal Society: The Good, the Bad and the Ugly
Wikipedia at the Royal Society: The Good, the Bad and the UglyWikipedia at the Royal Society: The Good, the Bad and the Ugly
Wikipedia at the Royal Society: The Good, the Bad and the UglyDuncan Hull
 
Improving the troubled relationship between Scientists and Wikipedia
Improving the troubled relationship between Scientists and Wikipedia Improving the troubled relationship between Scientists and Wikipedia
Improving the troubled relationship between Scientists and Wikipedia Duncan Hull
 
Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome Campus
Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome CampusBibliography 2.0: A citeulike case study from the Wellcome Trust Genome Campus
Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome CampusDuncan Hull
 
Accessing small molecule data using ChEBI
Accessing small molecule data using ChEBIAccessing small molecule data using ChEBI
Accessing small molecule data using ChEBIDuncan Hull
 
OWL-XML-Summer-School-09
OWL-XML-Summer-School-09OWL-XML-Summer-School-09
OWL-XML-Summer-School-09Duncan Hull
 
Authenticating Scientists with OpenID
Authenticating Scientists with OpenIDAuthenticating Scientists with OpenID
Authenticating Scientists with OpenIDDuncan Hull
 
The Invisible Scientist
The Invisible ScientistThe Invisible Scientist
The Invisible ScientistDuncan Hull
 
myExperiment @ Nettab
myExperiment @ NettabmyExperiment @ Nettab
myExperiment @ NettabDuncan Hull
 
The Year of Blogging Dangerously
The Year of Blogging DangerouslyThe Year of Blogging Dangerously
The Year of Blogging DangerouslyDuncan Hull
 
eScience: A Transformed Scientific Method
eScience: A Transformed Scientific MethodeScience: A Transformed Scientific Method
eScience: A Transformed Scientific MethodDuncan Hull
 
Defrosting the Digital Library: A survey of bibliographic tools for the next ...
Defrosting the Digital Library: A survey of bibliographic tools for the next ...Defrosting the Digital Library: A survey of bibliographic tools for the next ...
Defrosting the Digital Library: A survey of bibliographic tools for the next ...Duncan Hull
 
The Future of Research (Science and Technology)
The Future of Research (Science and Technology)The Future of Research (Science and Technology)
The Future of Research (Science and Technology)Duncan Hull
 
Chemical named entity recognition and literature mark-up
Chemical named entity recognition and literature mark-upChemical named entity recognition and literature mark-up
Chemical named entity recognition and literature mark-upDuncan Hull
 
Chemoinformatics and information management
Chemoinformatics and information managementChemoinformatics and information management
Chemoinformatics and information managementDuncan Hull
 
Text mining tools for semantically enriching scientific literature
Text mining tools for semantically enriching scientific literatureText mining tools for semantically enriching scientific literature
Text mining tools for semantically enriching scientific literatureDuncan Hull
 
Issues for metabolomics and
Issues for metabolomics and Issues for metabolomics and
Issues for metabolomics and Duncan Hull
 

Plus de Duncan Hull (20)

Why study plants?
Why study plants?Why study plants?
Why study plants?
 
Embedding employability in the Computer Science curriculum
Embedding employability in the Computer Science curriculumEmbedding employability in the Computer Science curriculum
Embedding employability in the Computer Science curriculum
 
Wikipedia at the Royal Society: The Good, the Bad and the Ugly
Wikipedia at the Royal Society: The Good, the Bad and the UglyWikipedia at the Royal Society: The Good, the Bad and the Ugly
Wikipedia at the Royal Society: The Good, the Bad and the Ugly
 
Improving the troubled relationship between Scientists and Wikipedia
Improving the troubled relationship between Scientists and Wikipedia Improving the troubled relationship between Scientists and Wikipedia
Improving the troubled relationship between Scientists and Wikipedia
 
Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome Campus
Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome CampusBibliography 2.0: A citeulike case study from the Wellcome Trust Genome Campus
Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome Campus
 
OWL and OBO
OWL and OBOOWL and OBO
OWL and OBO
 
Accessing small molecule data using ChEBI
Accessing small molecule data using ChEBIAccessing small molecule data using ChEBI
Accessing small molecule data using ChEBI
 
How to Blog
How to BlogHow to Blog
How to Blog
 
OWL-XML-Summer-School-09
OWL-XML-Summer-School-09OWL-XML-Summer-School-09
OWL-XML-Summer-School-09
 
Authenticating Scientists with OpenID
Authenticating Scientists with OpenIDAuthenticating Scientists with OpenID
Authenticating Scientists with OpenID
 
The Invisible Scientist
The Invisible ScientistThe Invisible Scientist
The Invisible Scientist
 
myExperiment @ Nettab
myExperiment @ NettabmyExperiment @ Nettab
myExperiment @ Nettab
 
The Year of Blogging Dangerously
The Year of Blogging DangerouslyThe Year of Blogging Dangerously
The Year of Blogging Dangerously
 
eScience: A Transformed Scientific Method
eScience: A Transformed Scientific MethodeScience: A Transformed Scientific Method
eScience: A Transformed Scientific Method
 
Defrosting the Digital Library: A survey of bibliographic tools for the next ...
Defrosting the Digital Library: A survey of bibliographic tools for the next ...Defrosting the Digital Library: A survey of bibliographic tools for the next ...
Defrosting the Digital Library: A survey of bibliographic tools for the next ...
 
The Future of Research (Science and Technology)
The Future of Research (Science and Technology)The Future of Research (Science and Technology)
The Future of Research (Science and Technology)
 
Chemical named entity recognition and literature mark-up
Chemical named entity recognition and literature mark-upChemical named entity recognition and literature mark-up
Chemical named entity recognition and literature mark-up
 
Chemoinformatics and information management
Chemoinformatics and information managementChemoinformatics and information management
Chemoinformatics and information management
 
Text mining tools for semantically enriching scientific literature
Text mining tools for semantically enriching scientific literatureText mining tools for semantically enriching scientific literature
Text mining tools for semantically enriching scientific literature
 
Issues for metabolomics and
Issues for metabolomics and Issues for metabolomics and
Issues for metabolomics and
 

Dernier

Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 

Dernier (20)

Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 

The Seven Deadly Sins of Bioinformatics

  • 1. The Seven Deadly Sins of Bioinformatics Professor Carole Goble [email_address] The University of Manchester, UK The myGrid project OMII-UK
  • 2.
  • 3. Intractable Problems in Bioinformatics. Have we sinned? Are these part of the intractable problem?
  • 4.
  • 5.
  • 6.
  • 7. They came up with more than seven. But I beat them into submission. Many are highly inter-related. Hopefully they are all too familiar.
  • 8.
  • 9.
  • 10.
  • 11. Comparative Genomics? Tisk! Its Comparative Bioinformatics Bioinformatics is about mapping one schema to another, one format to another, one id scheme to another. What a waste of time. What a handy distraction from doing some Real Science™.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. The “Oh No” OBO Pragmatists Aesthetics Philosophers Life Scientists Capulets Knowledge Representation Montagues A means to an end Content providers Theoreticians The end Mechanism providers Spiritual guides The Montagues and The Capulets …SOFG 2004, KCap 2005, Comparative and Functional Genomics 2004 Endurants, Perdurants, Being, Substance, Event
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31. A few months in the laboratory (or the computer) can save a few hours in the library (or on Google). Westheimer's Law (with additions).
  • 32.
  • 33.
  • 34. Not just bioinformatics Computer Science is Guilty!
  • 35. Why don’t biologists modularise OWL ontologies properly? Er, well, like how should we do it “properly” and where are the tools to help us? We don’t know and we haven’t got any. But here are some vague guidelines. W3C Semantic Web for Life Sciences mailing list, 2005
  • 36. “ I don't blame them [MGED/PSI community] because to truly comprehend RDF/OWL is not an easy task, it takes not just the understand of technology itself but more so the vision on how things should and can work in SW.” “ One thing we have to remember is that biologists are building ontologies to do a job of work. They are not produced as some end of CS or SW research” “ Principles are all well and good, but we should know from decades of software engineering that saying "do it properly" isn't a solution. We need tooling and methodologies that do not in themselves hinder a domain specialist. In many cases it is easier to re-develop than re-use or even cut-and-paste from an existing ontology than it is to muck around “doing it properly”” “ There is actually a gap between the view of ontology for CS people and for biological people. The ontology in biologist's eyes are more of a treaty than logical representation, that in CS view is on the reverse of that view. It needs dialog to bring the view to a middle ground and mechanisms to stretch to both directions.”
  • 37.
  • 38.
  • 39.
  • 40. Trust I don’t trust your code I don’t trust your data I don’t trust you will still be around in 1 year
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53. The myGrid Semantic Sweatshop notice how tired they look Franck Tanoh Katy Wolstencroft
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
  • 59. A good User Experience outweighs smart features. Can I use it? Is the user interface familiar? Does it fit with my needs?
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66. Distributed Annotation System Mash-Up http://www.biodas.org Reference Server AC003027 AC005122 M10154 Annotation Server Annotation Server AC003027 M10154 WI1029 AFM820 AFM1126 WI443 AC005122 Annotation Server
  • 67.
  • 68.
  • 69.
  • 70.
  • 71.
  • 72.
  • 73. “ No experiment is reproducible.” Wyszowski's Law “ An experiment is reproducible until another laboratory tries to repeat it.” Alexander Kohn
  • 74.
  • 75.
  • 76. “ I am sure one could reuse large parts of re-annotation for building transcriptome maps, if they only used workflows and ontologies”. Marco Roos A Biologist and Bioinformatician VL-e Project, Amsterdam
  • 77. “ Bioinformaticians have reached the standards of the 1980s, while computer scientists are working on the standards of the 2020s, leaving roughly 40 years to bridge. Marco Roos A Biologist and Bioinformatician VL-e Project, Amsterdam
  • 78.
  • 79.
  • 80.
  • 81.
  • 82. Sin Summary Maybe only one “original sin” in bioinformatics. Parochialism and Insularity Exceptionalism Autonomy or death! Vanity: Pride and Narcissism Monolith Meglomania Scientific method Sloth Instant Gratification Reinvention Churn
  • 83. Can we become less sinful? Why do these sins exist? Are bioinformaticians particularly naughty? No naughtier than Computer Scientists. And its all very hard. Though they are naughty…
  • 84.
  • 85.
  • 86.
  • 87.
  • 88. FaceBook & Bazaar for Workflow e-Scientists myexperiment.org Trials start August 2007!
  • 90.
  • 91.
  • 92.
  • 93.
  • 94. The Final Word Sin writes histories, goodness is silent.   Thomas Fuller

Notes de l'éditeur

  1. Ide