Cancer has myriad effects on metabolism that include both rewiring of intracellular
metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor …

The metabolome of an organism depends on environmental factors and intracellular
regulation and provides information about the physiological conditions. Metabolomics helps …

The increasing availability of large-scale single-cell atlases has enabled the detailed
description of cell states. In parallel, advances in deep learning allow rapid analysis of newly …

Motivation Machine learning (ML) methods are motivated by the need to automate
information extraction from large datasets in order to support human users in data-driven …

Biomedical data are amassed at an ever-increasing rate, and machine learning tools that
use prior knowledge in combination with biomedical big data are gaining much traction 1, 2 …

Signal transduction governs cellular behavior, and its dysregulation often leads to human
disease. To understand this process, we can use network models based on prior …

In recent years, there has been a surge of interest in using machine learning algorithms
(MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug …

The analysis of omic data depends on machine‐readable information about protein
interactions, modifications, and activities as found in protein interaction networks, databases …

Multi-omics analysis is a powerful and increasingly utilized approach to gain insight into
complex biological systems. One major hindrance with multi-omics, however, is the lengthy …

Microbiome science as an interdisciplinary research field has evolved rapidly over the past
two decades, becoming a popular topic not only in the scientific community and among the …