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Wim de Grave: Big Data in life sciences

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Talk by Wim de Grave on the 1st Symposium of Big Data and Public Health, 2013

Talk by Wim de Grave on the 1st Symposium of Big Data and Public Health, 2013

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  • 1. Big Data in Life Sciences 1st Symposium on Big Data and Public Health FGV 24/10/2013
  • 2. Big Data “Big Data in the life sciences sector is now a strategic and operational issue for almost all stakeholders. Capturing, storing, data flow and analysis of information-rich processes affect all aspects of the pharmaceutical and medical device industries but particularly the discovery, research & development stages”. “A strategic shift towards big data and data-driven approaches must be implemented from senior managers and thoroughly rolled-out across the organization”. Capturing Storing Data flow Analysis Summarize Represent
  • 3. Life Sciences – drug development - Drug discovery process - lead / target identification and research follow-up - Translation to clinical stages - Why & How of implementing big data approaches - Genomic & personalized medicines - combination of biomarkers research and retrospective data analysis, and analysis of current clinical outcomes - Systems Biology & detailed modeling & simulation processes - Better design NMEs, re-engineer and re-initiate previously failed drug programs - Design, collect & manage clinical stage data - Selecting EDC technologies, outsourcing Data Management responsibilities - Retain control over data quality - Real-world big data (Real World Evidence - RWE) for drug safety & surveillance for regulators in post-marketing, feeding back vital insights into mechanisms of action and real-life prescription and use - Understand product health outcome benefits for regulators, payers & other stakeholders: Product’s effectiveness, associated health outcomes and cost effectiveness endpoints - Manage and integrate data generated at all stages of the value chain, from discovery to realworld use after regulatory approval In all fields, the amount of data to be collected and managed has massively increased.
  • 4. Life Sciences – R&D  Genomics – metagenomics: generation of genetic code data  Clinical genomics: pharmacogenomics, disease marker genes etc  Expression studies: transcriptomics and micro-array  Protein structure, function and protein-protein interaction (also RNA/DNA/protein; saccharide, lipids etc)  System Biology – metabolic systems and their regulation, synthetic biology  Mass spectrometry analysis (biomarkers; complex mixtures)  Metabolomics; biodiversity extracts and fractionation  Phylogeny, Networks of life  Clinical Research, epidemiology, models  Public health  Scientific Literature and Patents – data and text mining
  • 5. C. Probst, Fiocruz Paraná
  • 6. C. Probst, Fiocruz Paraná
  • 7. aaacgcggaccgcacggtctgataggcaagttccggtatcgctattaccagggcagtcat cgcttgctgtaaccggttatgggttctgtcgtcaccaacgctatgggcacttcagttggc atgtttttctgcggataggtagcgatacgctgttgcgtcaccaaattccaaccacagaag ccggtataccgcgatcggttggtgtgcctgtgtttatgccttaccgtaaggaaagcaaca ggattaaggcgatagtgcgggtgacttcaatgatcgacgcaccgagccgaccggtcccag tgtgtatcaacacgtcgctagcgcgggtgtagtcgcgtattgctgctgtagcggtcattg tcttactgtccatcgacagcgaggatttgagacgcacgatatgtgacaaaatttgagaca tcgcgaccaagtagtggggaagtgatgtttcatcggaggtctcgtgtcattgtggcttgt ggtcgttgtctttcgatcttgacactccggcaaaaatatggtttatgccgaaatggccgt aatcacgggtattgggtgtcggcgccgggaagaattggttgtgttggccggccagtatgt tgatcgcgtcgggcttgtgggttttgctgatgatctgcagcgttttgccgacgaacggcc ggagagtagggttcggatcgaactgtgaccggtagatttcctcggatcagaacgaatcgg aacgattgctttgcgcagatatacaggccatagcgaaggtccggtactatcggtgtgtcg gtattcgcacgccacgaaaacgttgacctccactcaggcctaaccgttaccgtcaaaagt ttggatcgccactatacggtgaatatgcgagctacttggctgttgatcaaagtgcttgct aagcgttggccggcaacaggtagaagcgtggtggcgctcaccagtgatcacacaatgaat aacctaccctacggggctacgaaagccgtaatagatcgaattgtgcttgctgctgcctac gcactagggtgttcaagccgtgctcgccaacgtgatcaattcgggcccgggcgacattgg ctggatgacaccccgacctccagacgcgattaacctctatgcaaccgcccggatgtttag gaaaccctaaaagacttccaacttggtgtgcgctttctgctgtccgactactggcagtag gttaacggccagctcatccactgcaacggcagtttctccaaagaccaactccatgtctgc gttagtgcaacatgcagaaaactatggtatcattcctgttatctcgcattcagctgggct aagtctggccgcccacggttgtaagcgccgtggcggattgtgcattccggcgctgtcgtc cgatcgtggcgaagtagtaggcaagcgggaaagaaaagctagaagcaaaaaacagccacg gacaccgcatcccgctccggtagctataaacactggcagcagaatcatattcgtaacgaa gtagtcacagttgcccgaaacagcggttgggttggtgatctgcatccgcaggaaatgcgg atagctttccggtccctggaccaggttaccctgcccggccccatcgtgcacacagcgtgt attgaaatatcatcttagtatggtagccgctataccaactatgaagtgcccgcactttgt ggagaaaaagacggctttccagtaagtttggtataaaactgtggttttgacgtggttatc tagccgatagcggataggttacggactgtgtggacaagaagcgagatcatgggtagtgtg gccatgccatggtggactagggatcacatgcattcccggttacaattccggttgtgcaga gctggagggcctgtgcagttaaccgtgttgactcagcagttcatcttccagtgcgaggaa ctcgtcggacctagttcgtggagtaaacgccgggctcagccggagcttgggcccgtccaa ggtaatcaagatcgacctgaatagcaggtatgagtcaagttttagctagcggtggaaatc gagggttccccaaatgcgtaaccactgaaagaataggattaacgcttcggctttcatacc agcatcgctttcagcgcaattaccttcgacgtgccagaaggaaagtgatagcggtgcaac gtgattaccacgtgatccagctggacaaagccttagtcctaagattcctgcagaattgaa gtaattttcagaaactccgcaactggtggcaccgtgaggtgagaaaacggccgtcatcca atcgccattgttacctatacagattacaggtcggtatgtttaccgtgcggtctgccgccg aacttcaatagatcggttttgacatgggggaagatccgctgaatctccttgcagtacaga gtgatcgatgcgcaatcctaatgttgtctaggctaaccagctatcgtcttaagcaatgtt ctcgtccagtcagacatgttgaagaacgtgtacagatattcgttgtagccaccggtccgc caagccttaaggcacgtggacaagaagatggtgttgatccagtccggttcgtgattaagc actactggtaagtaacaactccggcactatctacgaatccgtagaatagtttcataatta gaaatctgctagcgcttgagcatgtttcggaaagtccaaaactacagtttcaagcacgat aatcaattcgacaagatatccggttctgtcgctgataacgttgctttgcaacatgatcgg ttcgaacaacacgcgccacctctctagcagaagatcactttctgcgatctcccaatttgc ctgcttcgcattaagtacggaagccatctgttcggcatagtcggtgatgtaggcggactg tttggtgttgaagatagccacaattttctcgacctggagtaaatggttcagtgaattcag tatcctgccatcgcaccagaggatcgactcgataaaatcagcaagtcacgtcagcgcccg ttcctgtgtatctgatccaggaacaccatgttgaggtagcgcagcaaatcgtggtacgaa aatgactgcggcactatacaggtggtcctcatctgagtgatgtatagatgcgcactgtcc atatgacgttggcgtttctgggtagctaatatacccttggcacccgcaggcatgtcgtag aaattagataccatgtcgctccgaaagtattgcagtagatgtatacaaacgtggaagaac taagatgtcaatgatttcaagttgacagggcgagcgtagtttatgttgaaaacctttgct gtgtagtcagaaactgctgccgtcgagtagctgatcgggctgacgttggggtccgcaggc tatgctcgtgacgttgagcttgcctttggtttcggtcaggcggtgcttgaccgagttggt All you wanted to know, but were afraid to ask...
  • 8. Adams, M.D., Kelley, J.M., Gocayne, J.D., Dubnick, M., Polymeropoulos, M.H., Xiao, H., Merril, C.R., Wu, A., Olde, B., Moreno, R., Kerlavage, A.R., McCombie, W.R., and Venter, J.C. Complementary DNA sequencing: "expressed sequence tags" and the human genome project. Science 252, 1651-1656 (1991).
  • 9. T. Otto 12
  • 10. General Proline and Arginine Metabolism
  • 11. In silico biochemistry Metabolism and solute transport of A. fulgidus Klenk et al, Nature n390, 364 (1997)
  • 12. human Drosophila mouse C. elegans Vibrio cholera Plasmodium Rickettsia Archaeoglobus Campilobacter Aquifex M. leprae Neisseria Chlamidia M. tuberculosis Arabidopsis Xylella rat Buchenerasp Ureaplasma Helicobacter E. coli Thermoplasma Borrelia Yersinia Ralstonia S. cerevisiae Thermotoga Bacillus Pseudomonas S. pombe Salmonella