- Unique identifier:
- Web user interface
- Input data:
- For search: a drug name (e.g., Cathine), a drug ID (e.g., D07627), a protein name (e.g., GABRA), a protein ID (e.g., hsa:4988); For prediction: the data files for drugs or target proteins in the format of either "profile" matrix or "kernel" similarity matrix. Input format: KEGG, MOL, STRING, TXT
- Computer skills:
- Drug-target Interaction Network Inference Engine based on Supervised Analysis
- Restrictions to use:
- Output data:
- A weighted bipartite graph with drugs and proteins as nodes and prediction scores for drug– protein pairs as edges. The prediction results are provided in the following ways: Inferred list, BRITE mapping, Pathway mapping and Downloadable text files.
- Susumu Goto <>
No open topic.
(Yamanishi et al., 2014)
DINIES: drug-target interaction network inference engine based on supervised analysis.
Nucleic Acids Res.
PMID: 24838565 DOI: 10.1093/nar/gku337
Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan; Institute for Advanced Study, Kyushu University, Higashi-ku, Fukuoka, Japan; Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
Supported by Ministry of Education, Culture, Sports, Science and Technology of Japan; the Japan Science and Technology Agency and the Japan Society for the Promotion of Science, JSPS KAKENHI grant , Program to Disseminate Tenure Tracking System, MEXT, Japan and Kyushu University Interdisciplinary Programs in Education and Projects in Research Development.
0 user reviews
0 user reviews
No review has been posted.