Combining semantic relations and DNA microarray data for novel hypotheses generation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6004 LNBI, Issue , 2010, Pages 53-61

  Hristovski, Dimitar ^a   Kastrin, Andrej ^b   Peterlin, Borut ^b   Rindflesch, Thomas C ^c  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b UNIVERSITY MEDICAL CENTRE LJUBLJANA   (Slovenia)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Literature based discovery; Microarray analysis; Natural language processing; Semantic predications

Indexed keywords

BIOMEDICAL LITERATURE; BIOMEDICAL RESEARCH; DNA MICROARRAY DATA; HYPOTHESES GENERATION; MICROARRAY ANALYSIS; MICROARRAY EXPERIMENTS; NATURAL LANGUAGE PROCESSING; POTENTIAL SOLUTIONS; SEMANTIC RELATIONS; TEXT-ANALYSIS METHODS;

COMPUTATIONAL LINGUISTICS; GENES; NATURAL LANGUAGE PROCESSING SYSTEMS;

SEMANTICS;

EID: 77953736609     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13131-8_7     Document Type: Conference Paper

References (22)

1. Cordero, F., Botta, M., Calogero, R.A.: Microarray data analysis and mining approaches. Brief Funct. Genomic Proteomic 6, 265-281 (2007)
2. Rindflesch, T.C., Fiszman, M.: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J. Biomed. Inform. 36, 462-477 (2003)
3. Barrett, T., Troup, D.B., Wilhite, S.E., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I.F., Soboleva, A., Tomashevsky, M., Edgar, R.: NCBI GEO: Mining tens of millions of expression profiles - database and tools update. Nucleic Acids Res. 35 (Database issue), D760-D765 (2007)
4. Shatkay, H., Edwards, S., Wilbur, W.J., Boguski, M.: Genes, themes and microarrays: using information retrieval for large-scale gene analysis. In: Proc. Int. Conf. Intell. Syst. Mol. Biol., pp. 317-328 (2000)
5. Blaschke, C., Oliveros, J.C., Valencia, A.: Mining functional information associated with expression arrays. Funct. Integr. Genomics 1, 256-268 (2001)
6. Yang, J., Cohen, A.M., Hersh, W.: Automatic summarization of mouse gene information by clustering and sentence extraction from MEDLINE abstracts. In: AMIA Annu. Symp. Proc., pp. 831-835 (2007)
7. Leach, S.M., Tipney, H., Feng, W., Baumgartner, W.A., Kasliwal, P., Schuyler, R.P., Williams, T., Spritz, R.A., Hunter, L.: Biomedical discovery acceleration, with applications to craniofacial development. PLoS Comput. Biol. 3, e1000215 (2009)
8. Jelier, R., 't Hoen, P.A., Sterrenburg, E., den Dunnen, J.T., van Ommen, G.J., Kors, J.A., Mons, B.: Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease. BMC Bioinformatics 9, 291 (2008)
9. Burkart, M.F., Wren, J.D., Herschkowitz, J.I., Perou, C.M., Garner, H.R.: Clustering microarray- derived gene lists through implicit literature relationships. Bioinformatics 23, 1995-2003 (2007)
10. Swanson, D.R.: Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect Biol. Med. 30, 7-18 (1986)
11. Hristovski, D., Peterlin, B., Mitchell, J.A., Humphrey, S.M.: Using literature-based discovery to identify disease candidate genes. Int. J. Med. Inform. 74, 289-298 (2005)
12. Hristovski, D., Friedman, C., Rindflesch, T.C., Peterlin, B.: Exploiting semantic relations for literature-based discovery. In: AMIA Annu. Symp. Proc., pp. 349-353 (2006)
13. Ahlers, C.B., Hristovski, D., Kilicoglu, H., Rindflesch, T.C.: Using the literature-based discovery paradigm to investigate drug mechanisms. In: AMIA Annu. Symp. Proc., pp. 6-10 (2007)
14. Masseroli, M., Kilicoglu, H., Lang, F.M., Rindflesch, T.C.: Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease. BMC Bioinformatics 7, 291 (2006)
15. Ahlers, C.B., Fiszman, M., Demner-Fushman, D., Lang, F.M., Rindflesch, T.C.: Extracting semantic predications from Medline citations for pharmacogenomics. In: Pac. Symp. Biocomput., pp. 209-220 (2007)
16. Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program. In: Proc. AMIA Symp., pp. 17-21 (2001)
17. Tanabe, L., Wilbur, W.J.: Tagging gene and protein names in biomedical text. Bioinformatics 18, 1124-1132 (2002)
18. R Development Core Team.: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
19. Gentleman, R.C., et al.: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)
20. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy Stat. Soc. B 57, 289-300 (1995)
21. Moran, L.B., Duke, D.C., Deprez, M., Dexter, D.T., Pearce, R.K., Graeber, M.B.: Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson's disease. Neurogenetics 7, 1-11 (2006)
22. White, L.R., Toft, M., Kvam, S.N., Farrer, M.J., Aasly, J.O.: MAPK-pathway activity, Lrrk2 G2019S, and Parkinson's disease. J. Neurosci. Res. 85, 1288-1294 (2007) (Pubitemid 46724372)