ICT for a Global Infrastructure for Health Research. Martin-Sanchez F. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)

1. ICT for a Global
Infrastructure for Health
Research
Dr. Fernando Martin-Sanchez
Director, Medical NanoBioInformatics Dept.
Institute of Health “Carlos III”
Madrid, Spain

2. Objectives of the session
• Background
- Biomedical research is an information intensive activity
- There exist new avenues in biomedical research
- New data types (extremely complex and heterogeneous)are being
generated at an unprecedented pace
• Main issues
- How can we collect, store, integrate and process this information -
high throughput - distributed computing?
- How can we use research data to model and simulate human
physiology and pathology?
- How can we promote the use of the EHR for research?

3. Participants
Dr. Fernando Martin-Sanchez.
Instituto de Salud Carlos III.
Prof. Vicente Hernandez Universitat
Politécnica de Valencia
Prof. Alex Frangi, University Pompeu
Fabra, Barcelona
Dr. Octavian Purcarea, Chief
Research and Strategy Officer.
Microsoft

4. Background
• New trends in medicine
• Data collection
- The “Nanoscope”
- High-throughput sequencing
- High-throughput phenotyping
• Data integration
• Data analysis and decision support

5. Genomic medicine

6. Why personalised medicine?
• To develope individualized
treatment regimes to avoid failures,
inefficiency and adverse reactions
related to drug therapy
• To facilitate early diagnosis and
advance in risk profiling, disease
prediction and prevention
• To improve disease classification
systems
• Growing health system costs

7. Why now?
• Advances in Information
Technologies
• Results from the Human Genome
Project and the Human Genetic
Variation Map (Hapmap)
• Laboratory technologies for
personal genome sequencing
• Growing knowledge about
molecular causes of disease

8. EC support to personalised
medicine (2001-)

9. New trends in medicine
• Genomic (molecular, personalised) medicine
• Regenerative medicine/tissue engineering
seeks to develop functional cell, tissue, and
organ substitutes to repair, replace or enhance
biological function that has been lost due to
congenital abnormalities, injury, disease, or
aging (NIH Definition, NIBIB, June 2004)
• NanoMedicine – Use of nanoscale tools and
components for the diagnosis, prevention and
treatment of diseases and for understanding
their pathophysiology (European Science
Foundation, Nov. 2005)

10. Why nano and regenerative
medicine?
• Cellular function takes place at the Nano
level: molecular nano-machines
• There are several nano-objects that can
produce disease (LDL, viruses, pollutans)
• The cause of the disease can be “nano”
but treatment is now “micro” or “macro”
• Advances in tissue engineering, cell and
gene therapy

11. ActionGrid - EC project
• ACTION-Grid is an international cooperative
support action on
- Medical Informatics
- Bioinformatics
- Grid Computing
- Nanoinformatics
• from Jun 08 to Jun 10
• Coordination. Prof. Victor Maojo - UPM

12. Data collection
F. Martin-Sanchez. “New Technologies and Applications Towards
Genomic Medicine”. En XIX Image Analysis Course of the Univ. La
Laguna, Personalized virtual medicine (p-Health) 6th-19th March 2006.
CATAI: 2006, 68-73 pp.

13. Data collection: The “Nanoscope”
i.e.: i.e.:
DNA Transdermal
ultrasequencers glucose monitoring
i.e.:
Nanosensors for
Radiation, contamination,
Toxicity)
Martín-Sanchez et al. “A primer in knowledge management for
Nanoinformatics in Medicine”. IOS-Press Proceedings 12th
International Conference on Knowledge-Based Intelligent
Information & Engineering Systems KES2008.

14. Information processing in
Nanomedicine - Nanoinformatics
http://www.nanotech.neu.edu/medicine/
Maojo, Martín-Sanchez et al. “”Nanoinformatics and DNA computing:
catalizing nanomedicine”. (2010) Pediatric Research. Special issue on
Nanomedicine.

15. High-throughput sequencing
Fred Sanger
15 September 2005
Volume 437 Number
7057 pp376-380

16. Human genomes sequenced
up to now
• James Watson, 454. $70 mill
• Craig Venter, Sanger, - $1 mill.
• African - HapMap – Illumina & Solid, $100.000
• Five african – Penn State Univ.
• Chinese, Illumina
• Two koreans
• Prof. Quake - Stanford - - Nature genetics paper -
$50.000, 1 week, Helicos SMS . Stanford team -
Clinical annotation of genome from “patient Zero”
• Drug metabolism
• Rare genetic variants - rare diseases
• Common genetic variants - Risk of complex
diseases

17. High throughput phenotyping
• Disease specific algorithms scanning across
electronic medical records - generate structured
,standardized, anonymized, clinical data sets
for research
• Important issues:
• NLP on administrative, laboratory and medical data
• Reproducibility and standardisation
• Privacy and confidenciality

18. Data integration
• Ontologies
• NCBO Bioportal
• 168 ontologies: from
Nanomedicine to
public health
• Browser, mappings,
visualization features
• Useful for annotation
of data resources

19. Data analysis: GWAS (Genome
Wide Association Studies)
• >500.000 SNPs, >2000
individuals
• Connecting molecular data
with clinical phenotypes
through system biology
approaches:
- genetic networks
- pathway analysis
- interaction maps
• Analysis methods
- Bayesian networks,Markov
graphs, Petri nets...

20. The central role of EHR

21. From data collection to medical
decision making

22. Final remark: from particle to
population
Altman RB, Balling R, Brinkley JF, Coiera E, Consorti F, Dhansay MA, Geissbuhler A, Hersh W,
Kwankam SY, Lorenzi NM, Martin-Sanchez F, Mihalas GI, Shahar Y, Takabayashi K,
Wiederhold G. "Commentaries on Informatics and medicine: from molecules to populations".
Methods Inf Med. 2008;47(4):296-317. PMID: 18690363