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Biomedical Databases & Tools Rolando Garcia-Milian Rolando.milian@ufl.edu Biomedical & Health Information Servic

Biomedical Databases & Tools Rolando Garcia-Milian Rolando.milian@ufl.edu Biomedical & Health Information Services Department Health Sciences Center Library October 3, 2013. Problem – Rapid Growth of Biomedical data .

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Biomedical Databases & Tools Rolando Garcia-Milian Rolando.milian@ufl.edu Biomedical & Health Information Servic

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  1. Biomedical Databases & Tools Rolando Garcia-Milian Rolando.milian@ufl.edu Biomedical & Health Information Services Department Health Sciences Center Library October 3, 2013

  2. Problem – Rapid Growth of Biomedical data GenBank Statistics http://www.ncbi.nlm.nih.gov/genbank/genbankstats-2008/ Compiled from GEO historic data http://www.ncbi.nlm.nih.gov/geo/summary/?type=history

  3. Problem – Growth of the Biomedical Literature Biomedical Literature • Huge volume (PubMed 23132342 citations) • High diversity • High quality (peer review) Compiled by from PubMed http://www.ncbi.nlm.nih.gov/pubmed • Users overwhelmed by long list of search results • 1/3 of Pubmed queries result in 100 or more citations (Islamaj, 2009)

  4. Problem – Querying the Biomedical Literature Querying the biomedical literature becomes more difficult Boolean operators Medical Subject Headings Filters

  5. Alternative Mining Tools for the Biomedical Literature Protein/gene associated Medical terminology Synonym Main gene query

  6. Alternative Mining Tools for the Biomedical Literature • Based on Universal Medical Language System • Repository of semantic predications (subject-predicate-object triples) • 57.6 million predications from all of PubMed citations (Rindflesch, 2011)

  7. Alternative Mining Tools for the Biomedical Literature Linked to Entrez Gene database

  8. Workshop- Novel Online Tools for Mining the Biomedical Literature

  9. Case 1 – Few Results in the Biomedical Literature • Searching for novel genes

  10. Case 2 – Few Results in the Biomedical Literature • Searching for side effects of drugs: Cerebyx – respiratory failure

  11. Case 2 – Few Results in the Biomedical Literature Phenotypic information can be used to infer molecular interactions and hinting at new uses of marketed drugs (Campillos, 2008)

  12. Freely Available Up-To-Date Discovery Tools European Bioinformatics Institute, UK National Center for Biotechnology Information, USA

  13. Proprietary Tools

  14. Annotation/ Visualization Tools – Genome Browsers

  15. Workshop- Introduction to Genome Browsers

  16. References Campillos M*, Kuhn M*, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008 Jul 11;321(5886):263-6. http://www.ncbi.nlm.nih.gov/pubmed/18621671 IslamajDogan R, Murray GC, Névéol A, Lu Z. (2009) Understanding PubMed user search behavior. Database (Oxford) http://www.ncbi.nlm.nih.gov/pubmed/20157491 Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genomebrowserat UCSC. Genome Res. 2002 Jun;12(6):996-1006. http://www.ncbi.nlm.nih.gov/pubmed/12045153 Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. MolSyst Biol. 2010;6:343. Epub 2010 Jan 19. http://sideeffects.embl.de/drugs/56338/ Rindflesch, T.C. et al. (2011) Semantic MEDLINE: An advanced information management application for biomedicine. Information Services & Use, 31, 15-21. http://lhncbc.nlm.nih.gov/system/files/pub-lhncbc-2011-109.pdf

  17. Data Resources • Data Management Resources, UF Libraries: http://library.ufl.edu/datamgmt • Surveys on Your Needs • UF Research Data Needs Assessment: http://bit.ly/UFdatasurvey • Big Data, Little Data Workshop Evaluation:  http://bit.ly/dataeval

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