The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

Abstract Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious
experiment via biochemical approaches. Machine learning based methods have been …

Unsupervised feature selection always occupies a key position as a preprocessing in the
tasks of classification or clustering due to the existence of extra essential features within high …

With the emergence of large-scale experimental data on genes and proteins, drug discovery
and repositioning will be more difficult in the field of biomedical research. More and more …

While the celebrated Word2Vec technique yields semantically rich representations for
individual words, there has been relatively less success in extending to generate …

Though most of the faces are axis-symmetrical objects, few real-world face images are axis-
symmetrical images. In the past years, there are many studies on face recognition, but only …

Collaborative representation is an effective way to design classifiers for many practical
applications. In this paper, we propose a novel classifier, called the prior knowledge-based …

Diseases are usually caused by body's own defects protein or the functional structure of viral
proteins. Effective drugs can be combined with these proteins well and remove original …

Abstract Analysis of drug–target interactions (DTIs) is of great importance in developing new
drug candidates for known protein targets or discovering new targets for old drugs. However …

In clustering analysis, data attributes may have different contributions to the detection of
various clusters. To solve this problem, the subspace clustering technique has been …