A comparison of machine learning techniques for detection of drug target articles

作者：Danger Roxana; Segura Bedmar Isabel^*; Martinez Paloma; Rosso Paolo

来源：Journal of Biomedical Informatics, 2010, 43(6): 902-913.

DOI：10.1016/j.jbi.2010.07.010

• Biomedical text classification
• Biomedical information retrieval
• Drug discovery
• Drug target
• Machine learning
• Support Vector Machines
• Naive Bayes
• Unified Medical Language System
• MetaMap

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 resource

• 出版日期2010-12