Machine learning, a collection of data-analytical techniques aimed at building predictive
models from multi-dimensional datasets, is becoming integral to modern biological research …

We summarise various ways of performing dimensionality reduction on high‐dimensional
microarray data. Many different feature selection and feature extraction methods exist and …

Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …

Proteins and their functional interactions form the backbone of the cellular machinery. Their
connectivity network needs to be considered for the full understanding of biological …

Abstract Background Personalized, precision, P4, or stratified medicine is understood as a
medical approach in which patients are stratified based on their disease subtype, risk …

Pathway analysis is often the first choice for studying the mechanisms underlying a
phenotype. However, conventional methods for pathway analysis do not take into account …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

Network embedding is to learn low-dimensional vector representations for nodes in a
network. It has shown to be effective in a variety of tasks such as node classification and link …

INTRODUCTION A disease is rarely a straightforward consequence of an abnormality in a
single gene, but rather reflects the interplay of multiple molecular processes. The …