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BIG DATA A Life Sciences Perspective

BIG DATA A Life Sciences Perspective. Scott Novogoratz, CIO College of Veterinary Medicine & Biomedical Sciences. Infectious Disease Research Center.

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BIG DATA A Life Sciences Perspective

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  1. BIG DATAA Life Sciences Perspective Scott Novogoratz, CIO College of Veterinary Medicine & Biomedical Sciences

  2. Infectious Disease Research Center Among the world's leaders in researching West Nile Virus, drug-resistant Tuberculosis, Yellow Fever, Dengue, Hantavirus, Plague, Tularemia and other zoonotic and human diseases

  3. Radiological Cancer Treatment Heavy Ion Therapy

  4. “BIG DATA is data that exceeds the processing capacity of conventional database systems.” Ed Dumbill, Big Data, Editor in Chief

  5. Omics & Ologies -Life Sciences BIG DATA Omics • Genomics, • Transcriptomics, • Proteomics, • Metabolomics, • Metagenomics BIG DATA Devices • Gene sequencing • Mass spectrometry • Imaging • Microarrays • Liquid chromatography Ology(ies) • Radiology • Gastroenterology, • Cardiology, • Pathology

  6. Increases due to: • Avg. Size/Study • More Digitized Data • Pathology • Endoscopy • Pictures • More Imaging Procedures Medical Imaging BIG DATA Demands

  7. Radiographs Canine Hip Dysplasia

  8. Endoscopy Canine Duodenum Endoscopy Procedure

  9. Pathology

  10. How Big is a Genome? E.Coli 4 Million Base Pairs Human 3 Billion Base Pairs Paris Japonica 152 Billion Base Pairs

  11. e.Coli

  12. The scale of biological data is exponentially increasing with sequencing technologies now producing data at a rate exceeding growth in computing power predicted by Moore’s Law (10,000-fold improvement in sequencing vs. 16-fold improvement in computing From the Big Data article Unraveling the Complexities, Higdon et al

  13. Velocity –Genome Studies Will Increase

  14. What Do Life Science Researchers Want? • Reliable Data • Statistically Valid Results • Analysis Tools with User-Friendly I/F • Transparent Reporting of Results • Ability to Share Data From U of Washington study to assess data & analysis needs for Life Scientists

  15. Relative Importance for the Life Sciences • Volume • Veracity • Velocity • Variety • Value

  16. Conclusions • Recognize that BIG DATA storage issues differ based on the purpose and use of data • Maximize the value of biological research, by improving the capability to store, catalog, share and compare research through: • Low cost and shared storage mechanisms • Universal and easy-to-use tools that provide researchers with the capability to compare their findings with libraries of information

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