Text mining functional keywords associated with genes.
Modern experimental techniques provide the ability to gather vast amounts of biological data in a single experiment (e.g. DNA microarray experiment), making it extremely difficult for the researcher to interpret the data and form conclusions about the functions of the genes. Current approaches provide useful information that organizes or relates genes, but a major shortcoming is they either do not address specific functions of the genes or are constrained by functions predefined in other databases, which can be biased, incomplete, or out-of-date. We extended Andrade and Valencia's method [1] to statistically mine functional keywords associated with genes from MEDLINE abstracts. The MEDLINE abstracts are analyzed statistically to score and rank keywords for each gene using a background set of words for baseline frequencies. We generally got very good functional keyword information about the genes we tested, which was confirmed by searching for the individual keywords in context. The keywords extracted by our algorithm reveal a wealth of potential functional concepts, which were not represented in existing public databases. We feel that this approach is general enough to apply to medical and biological literature to find other relationships: drugs vs. genes, risk-factors vs. genes, etc.
Liu
Ying
Y
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA. yingliu@cc.gatech.edu
Brandon
Martin
M
Navathe
Shamkant
S
Dingledine
Ray
R
Ciliax
Brian J
BJ
D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005796:Genes; D016247:Information Storage and Retrieval; D016239:MEDLINE; D020411:Oligonucleotide Array Sequence Analysis; D013223:Statistics as Topic; D013358:Subject Headings