DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

摘要

The flowchart of the DLDTI pipeline. DLDTI first integrates a variety of drug-related information sources to construct a heterogeneous network and applies a compact feature learning algorithm to obtain a low-dimensional vector representation of the features describing the topological properties for each node. Next, DLDTI determines the optimal mapping from the plenary mapping space to the prediction subspace, and whether the feature vector is close to the known correlations. Afterwards, DLDTI infers the new DTIs by ranking the candidates according to their proximity to the predicted subspace