Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan Institute for Advanced Study, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.
Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W39-45. doi: 10.1093/nar/gku337. Epub 2014 May 16.
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.
DINIES（基于监督分析的药物-靶标相互作用网络推断引擎）是一个网络服务器，用于在监督网络推断框架中，从各种类型的生物数据（如化学结构、药物副作用、氨基酸序列和蛋白质结构域）预测未知的药物-靶标相互作用网络。DINIES 的创新之处在于使用最先进的机器学习方法进行预测，在异构生物数据的集成以及与 KEGG 数据库的兼容性方面。DINIES 服务器以制表符分隔的文件格式接受任何药物和靶蛋白的“概况”或预先计算的相似性矩阵（或“核”）。当提交训练数据集以学习预测模型时，用户可以选择 KEGG DRUG 数据库中的已知相互作用信息或自己的相互作用数据。用户还可以选择用于监督网络推断的算法，选择方法中的各种参数，并为异构数据集成指定权重。该服务器可以提供与 KEGG 中有用组件的综合分析，如生物途径、功能层次结构和人类疾病。DINIES（http://www.genome.jp/tools/dinies/）作为 GenomeNet 中的基因组分析工具之一，可供公众使用。
