Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and
various cellular processes. The identification of disease-related miRNAs provides great …

Feature selection is an important approach for reducing the dimension of high-dimensional
data. In recent years, many feature selection algorithms have been proposed, but most of …

Microarray gene expression data gained great importance in recent years due to its role in
disease diagnoses and prognoses which help to choose the appropriate treatment plan for …

Recently, graph learning methods have attracted much research attention, which uses first-
order nearest-neighbor relation between pixels to construct adjacency graphs for capturing …

A large number of biological studies have shown that microRNAs (miRNAs) are closely
related to the occurrence and development of various human diseases. Nowadays, more …

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a
lot of attention in the field of machine learning and data mining, as well as the square loss …

As modern industrial processes become complicated, and some faults are difficult to be
detected due to noises and nonlinearity of data, data-driven fault detection (FD) has been …

Many semi-supervised learning methods have been developed in recent years, especially
graph-based approaches, which have achieved satisfactory performance in the practical …

In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given
nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve …

Background The aberrant expression of microRNAs is closely connected to the occurrence
and development of a great deal of human diseases. To study human diseases, numerous …