Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …

Rapid technological advances have led to the production of different types of biological data
and enabled construction of complex networks with various types of interactions between …

In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has
received increasing attention in various practical applications. However, most traditional …

Graph clustering based on embedding aims to divide nodes with higher similarity into
several mutually disjoint groups, but it is not a trivial task to maximumly embed the graph …

Data clustering, which is to partition the given data into different groups, has attracted much
attention. Recently various effective algorithms have been developed to tackle the task …

Non-negative matrix factorization (NMF) has become one of the most powerful methods for
clustering and feature selection. However, the performance of the traditional NMF method …

Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view
data shows impressive behavior in machine learning. Usually, multi-view data have …

Hyperspectral (HS) and multispectral (MS) image fusion aims at producing high-resolution
HS (HRHS) images. However, the existing methods could not simultaneously consider the …