| Feature selection for the imbalanced QSAR problems by using easyensemble. | |
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MedLine Citation:
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PMID: 20063462 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Activities of drug molecules can be predicted by Quantitative Structure Activity Relationship (QSAR) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an imbalanced situation. Here we propose one embedded feature selection algorithm i.e., Prediction Risk based feature selection for EasyEnsemble (PREE) to treat this problem and improve generalisation performance of the EasyEnsemble classifier. Experimental results on the drug molecules data sets show that PREE obtains better performance, compared with the asymmetric bagging and EasyEnsemble. |
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Authors:
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Tian-Yu Liu; Guo-Zheng Li; Jack Y Yang; Mary Qu Yang |
Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: International journal of computational biology and drug design Volume: 1 ISSN: 1756-0756 ISO Abbreviation: Int J Comput Biol Drug Des Publication Date: 2008 |
Date Detail:
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Created Date: 2010-01-11 Completed Date: 2010-03-24 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101479540 Medline TA: Int J Comput Biol Drug Des Country: England |
Other Details:
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Languages: eng Pagination: 334-46 Citation Subset: IM |
Affiliation:
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School of Electric, Shanghai Dianji University, Shanghai, China. liuty@sdju.edu.cn |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Drug Design* False Positive Reactions Humans Models, Biological Quantitative Structure-Activity Relationship* Risk Assessment Sensitivity and Specificity Treatment Outcome |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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