DINIES specifications


Unique identifier OMICS_05433
Alternative name Drug-target Interaction Network Inference Engine based on Supervised Analysis
Interface Web user interface
Restrictions to use None
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
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.
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Susumu Goto

Additional information


Publication for Drug-target Interaction Network Inference Engine based on Supervised Analysis

DINIES citations


Spectroscopic identification of active sites for the oxygen evolution reaction on iron cobalt oxides

Nat Commun
PMCID: 5722881
PMID: 29222428
DOI: 10.1038/s41467-017-01949-8

[…] cleaned substrates while spinning at 3000 r.p.m. (FTO substrates used for UV–visible spectroscopy, GC substrates used for XAS and electrochemistry). Subsequent irradiation by UV light (M1 UV-Chamber, Dinies Technologies GmbH) for 16 h yielded the metal oxide films. […]


Identification of candidate drugs using tensor decomposition based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets

Sci Rep
PMCID: 5653784
PMID: 29062063
DOI: 10.1038/s41598-017-13003-0

[…] At first, it was tested whether known drug target proteins were enriched among those identified by the present strategy. To obtain the list of known drug target proteins, I used DINIES. Although it can also infer unknown interactions between drugs and proteins, by uploading a single drug using a DINIES search with parameters ‘chemogenomic approach’ and ‘with learning on all D […]

DINIES institution(s)
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
DINIES funding source(s)
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 [25700029], Program to Disseminate Tenure Tracking System, MEXT, Japan and Kyushu University Interdisciplinary Programs in Education and Projects in Research Development.

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