Bonfring International Journal of Research in Communication Engineering
Online ISSN: 2277-5080 | Print ISSN: 2250-110X | Frequency: 4 Issues/Year
Impact Factor: 0.528 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)
Mining Multi Drug-Pathways via A Probabilistic Heterogeneous Network Multi-label Classifier
Abstract:
Mining drug networks is a very important research issue to discover hidden relations between multi drug-entities relations, such as multi drug-pathways, multi drug-targets, and multi drug-diseases. One very important relation is the drug-pathway, where drugs affect the human body through their pathways. In this paper, a probabilistic Heterogeneous Network Multi-label Classifier (HNMC) is proposed to classify multi drug-pathways relations. Data is collected from Drugbank.ca [1], Kegg (keg drug, Kegg diseases, Kegg pathways, Kegg orthologs, Kegg brite) [2] and small molecular pathways [3,4]. For drug-pathways data, two datasets are considered: one is based on Drug-Drug Interaction (DDI) and the other is based on Drug-Pathways Interactions (DPI). HNMC has proved its efficiency with an average of 90% precision, 92.35% recall, 92% accuracy, relojes de imitacion espa?a and 96% ROC
Keywords: Multi Drug-Pathways Prediction, Probabilistic Heterogeneous Networks and Multi-label Classification
Volume: 4 | Issue: 4
Pages: 10-16
Issue Date: November , 2014
DOI: 10.9756/BIJRCE.10368
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