mrphil / deepmpf

DeepMPF: Deep Learning Framework for Predicting Drug-Target Interactions based on Multi-Modal Representation with Meta-Path Semantic Analysis

A multi-modal representation framework of DTI prediction

Data details

This paper includes four comparison datasets that enzymes, G-protein-coupled receptors (GPCR), ion channels and nuclear receptors collected from reference [1] . Each dataset file contains the three modal information and expanded information of disease domain. Furthermore, There are examples of saving models each fold in the path of " Example/draw/metaPath/ 'dataset name' / 'method.h5' ".

Citation

Zhong-Hao Ren, Zhu-Hong You, Chang-Qing Yu, Yan-Fang Ma, Yong-Jian Guan, Hai-Ru You, Xin-Fei Wang and Jie Pan, DeepMPF: Deep Learning Framework for Predicting Drug-Target Interactions based on Multi-Modal Representation with Meta-Path Semantic Analysis

Title

DeepMPF: Deep Learning Framework for Predicting Drug-Target Interactions based on Multi-Modal Representation with Meta-Path Semantic Analysis

[1] Yamanishi Y, Araki M, Gutteridge A et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces, Bioinformatics 2008;24:i232-i240.