RT Journal Article
T1 A comparison of machine learning techniques for detection of drug target articles
A1 Danger, Roxana
A1 Segura-Bedmar, Isabel
A1 Martínez Fernández, Paloma
A1 Rosso, Paolo
AB Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular  structures whose abnormal activity, associated to a disease, can be  modified by drugs, improving the health of patients. Pharmaceutical  industry needs to give priority to their identification and validation  in order to reduce the long and costly drug development times. In the  last two decades, our knowledge about drugs, their mechanisms of action  and drug targets has rapidly increased. Nevertheless, most of this  knowledge is hidden in millions of medical articles and textbooks.  Extracting knowledge from this large amount of unstructured information  is a laborious job, even for human experts. Drug target articles  identification, a crucial first step toward the automatic extraction of  information from texts, constitutes the aim of this paper. A comparison  of several machine learning techniques has been performed in order to  obtain a satisfactory classifier for detecting drug target articles  using semantic information from biomedical resources such as the Unified  Medical Language System. The best result has been achieved by a Fuzzy  Lattice Reasoning classifier, which reaches 98% of ROC area measure.
PB Elsevier
SN 1532-0464
YR 2010
FD 2010-12
LK https://hdl.handle.net/10016/20287
UL https://hdl.handle.net/10016/20287
LA eng
NO This research paper is supported by Projects TIN2007-67407-C03-01, S-0505/TIC-0267 and MICINN project TEXT-ENTERPRISE2.0 TIN2009-13391-C04-03 (Plan I + D + i), as well as for the Juande la Cierva program of the MICINN of Spain
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RD 20 may. 2024