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 …

Drug discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …

Although demonstrating great success, previous multi-view unsupervised feature selection
(MV-UFS) methods often construct a view-specific similarity graph and characterize the local …

Graph based multi-view clustering has been paid great attention by exploring the
neighborhood relationship among data points from multiple views. Though achieving great …

Abstract Analysis and prediction of drug-target interactions (DTIs) play an important role in
understanding drug mechanisms, as well as drug repositioning and design. Machine …

Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force
search over a compound database is financially infeasible. We have witnessed the …

Multi-view unsupervised feature selection has been proven to be an effective approach to
reduce the dimensionality of multi-view data. One of its key issues is how to exploit the …

Graph neural networks (GNNs), which are capable of learning representations from
graphical data, are naturally suitable for modeling molecular systems. This review …

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from
multi-view data without using the labels of samples. However, we observe that existing MV …

Multi-view learning has become a significant research topic in image processing, data
mining and machine learning due to the proliferation of multi-view data. Considering the …