Abstract
Although microarray experiments have great potential to support progress in biomedical research, results are not easy to interpret. Information about the functions and relations of relevant genes needs to be extracted from the vast biomedical literature. A potential solution is to use computerized text analysis methods. Our proposal enhances these methods with semantic relations. We describe an application that integrates such relations with microarray results and discuss its benefits in supporting enhanced access to the relevant literature for interpretation of results and novel hypotheses generation. The application is available at http://sembt.mf.uni-lj.si
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cordero, F., Botta, M., Calogero, R.A.: Microarray data analysis and mining approaches. Brief Funct. Genomic Proteomic 6, 265–281 (2007)
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)
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)
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)
Blaschke, C., Oliveros, J.C., Valencia, A.: Mining functional information associated with expression arrays. Funct. Integr. Genomics 1, 256–268 (2001)
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)
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)
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)
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)
Swanson, D.R.: Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect Biol. Med. 30, 7–18 (1986)
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)
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)
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)
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)
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)
Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program. In: Proc. AMIA Symp., pp. 17–21 (2001)
Tanabe, L., Wilbur, W.J.: Tagging gene and protein names in biomedical text. Bioinformatics 18, 1124–1132 (2002)
R Development Core Team.: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
Gentleman, R.C., et al.: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hristovski, D., Kastrin, A., Peterlin, B., Rindflesch, T.C. (2010). Combining Semantic Relations and DNA Microarray Data for Novel Hypotheses Generation. In: Blaschke, C., Shatkay, H. (eds) Linking Literature, Information, and Knowledge for Biology. Lecture Notes in Computer Science(), vol 6004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13131-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-13131-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13130-1
Online ISBN: 978-3-642-13131-8
eBook Packages: Computer ScienceComputer Science (R0)