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, and
96% ROC
Research Department
Research Journal
Bonfring International Journal of Research in Communication Engineering,
Research Member
Research Rank
1
Research Vol
Vol. 4, No. 2
Research Website
http://journal.bonfring.org/abstract.php?id=6&archiveid=427
Research Year
2014
Research_Pages
pp.10-16
Research Abstract
