Paper Info
Title
DINIES: drug-target interaction network inference engine based on supervised analysis.
Abstract
DINIES (drug-target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug-target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any 'profiles' or precalculated similarity matrices (or 'kernels') of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases.
Year
DOI
Venue
2014
10.1093/nar/gku337
NUCLEIC ACIDS RESEARCH
Keywords
Field
DocType
internet,proteins,artificial intelligence,algorithms,drug discovery
File format,Data mining,Biological data,Biology,Inference,Interaction network,KEGG,Inference engine,Interaction information,Bioinformatics,Genetics,Web server
Journal
Volume
Issue
ISSN
42
W1
0305-1048
Citations 
PageRank 
References 
15
0.72
19
Authors
6
Name
Order
Citations
PageRank
Yoshihiro Yamanishi1126883.44
Masaaki Kotera228523.48
Yuki Moriya317816.67
Ryusuke Sawada4332.77
Minoru Kanehisa54429707.80
Susumu Goto63213541.48