350 rub
Journal Neurocomputers №9 for 2016 г.
Article in number:
Application of text mining for identification and characterization of "sexy" microRNAs
Keywords: neural-like text-mining associative networks automatic knowledge acquisition microRNA microRNA-21 micro-RNA-155 microRNA-34 microRNA-146 microRNA-200 microRNA-29 microRNA-17 microRNA-125 microRNA-122 microRNA-145
Authors:
A.A. Kechin - Assistant, Laboratory of Pharmacogenomics, Institute of Chemical Biology and fundamental Mekicine of Siberian branch of RAS (Novosibirsk) A.E. Kel - Senior Researcher, Institute of Chemical Biology and Fundamental Medicine of Siberian branch of RAS (Novosibirsk) N.E. Kushlinskii - Dr.Sc. (Med.), Professor, Member-Correspondent of Russian Academy of Sciences, Head of the Clinical Biochemistry Laboratory, N.N. Blokhin Russian Cancer Research Center (Moscow) M.L. Filipenko - Ph.Dr. (Biol.), Head of the Laboratory of Pharmacogenomics, Institute of Chemical Biology and Fundamental Medicine Siberian branch of the RAS (Novosibirsk)
Abstract:
The use of text mining allows us to not only extract the known data published in biomedical texts but also to synthesize new ones. The aim of our study was to identify the most frequently studied microRNAs and summarize the available information about them using PubMed database. For this purpose we performed a text mining of 37127 paper headlines and summaries. Most studied miRNAs were microRNA-21, -155, -34, -146, -200, -29, -17, -125, -122 and -145. We performed visualization of interconnection of miRNA with cellular processes and elements (mRNA transcription, proliferation, apoptosis, differentiation, survival), as well as with tissues and organs (-21 miRNA and miRNA-200 - epithelial tissues, miR-145 and microRNA-34 - with the nervous system; miR-122 - with the liver cells). The resulting aggregated data may be useful for the overall assessment of the role of microRNAs in health and disease.
Pages: 45-56
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