Combining Semantic Relations and DNA Microarray Data for Novel Hypotheses Generation

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

[1]  Joyce A. Mitchell,et al.  Using literature-based discovery to identify disease candidate genes , 2005, Int. J. Medical Informatics.

[2]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[3]  Halil Kilicoglu,et al.  Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease , 2006, BMC Bioinformatics.

[4]  Marcelo Fiszman,et al.  The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text , 2003, J. Biomed. Informatics.

[5]  D. Swanson Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[6]  Lawrence Hunter,et al.  Biomedical Discovery Acceleration, with Applications to Craniofacial Development , 2009, PLoS Comput. Biol..

[7]  Marcelo Fiszman,et al.  Extracting Semantic Predications from Medline Citations for Pharmacogenomics , 2006, Pacific Symposium on Biocomputing.

[8]  Hagit Shatkay,et al.  Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis , 2000, ISMB.

[9]  L. Moran,et al.  Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson’s disease , 2006, Neurogenetics.

[10]  Marco Botta,et al.  Microarray data analysis and mining approaches. , 2008, Briefings in functional genomics & proteomics.

[11]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[12]  Jonathan D. Wren,et al.  Clustering microarray-derived gene lists through implicit literature relationships , 2007, Bioinform..

[13]  A. Valencia,et al.  Mining functional information associated with expression arrays , 2001, Functional & Integrative Genomics.

[14]  Carol Friedman,et al.  Exploiting Semantic Relations for Literature-Based Discovery , 2006, AMIA.

[15]  Dennis B. Troup,et al.  NCBI GEO: mining tens of millions of expression profiles—database and tools update , 2006, Nucleic Acids Res..

[16]  Mathias Toft,et al.  MAPK‐pathway activity, Lrrk2 G2019S, and Parkinson's disease , 2007, Journal of neuroscience research.

[17]  William R. Hersh,et al.  Automatic Summarization of Mouse Gene Information by Clustering and Sentence Extraction from MEDLINE Abstracts , 2007, AMIA.

[18]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[19]  Peter A. C. 't Hoen,et al.  Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease , 2008, BMC Bioinformatics.

[20]  Halil Kilicoglu,et al.  Using the Literature-Based Discovery Paradigm to Investigate Drug Mechanisms , 2007, AMIA.

[21]  Lorraine K. Tanabe,et al.  Tagging gene and protein names in biomedical text , 2002, Bioinform..