AI Article Synopsis
- - DINIES is a web server that predicts unknown drug-target interactions using various biological data, like chemical structures and amino acid sequences, leveraging supervised machine learning methods.
- - The server allows users to upload similarity matrices of drugs and proteins and choose between known KEGG database interactions or their own data for training predictive models.
- - DINIES also offers integration with the KEGG database for analysis of biological pathways, functional hierarchies, and diseases, making it a valuable tool for researchers.
Article 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. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086078 | PMC |
| http://dx.doi.org/10.1093/nar/gku337 | DOI Listing |
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