Liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS) has
become a workhorse in global metabolomics studies with growing applications across …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …

Increased availability of high-throughput technologies has generated an ever-growing
number of omics data that seek to portray many different but complementary biological …

To study complex biological processes holistically, it is imperative to take an integrative
approach that combines multi-omics data to highlight the interrelationships of the involved …

Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine
two or more omics data sets to aid in data analysis, visualization and interpretation to …

Abstract Characterization and integration of the genome, epigenome, transcriptome,
proteome and metabolome of different datasets is difficult owing to a lack of ground truth …

Multi-omics data analysis is an important aspect of cancer molecular biology studies and
has led to ground-breaking discoveries. Many efforts have been made to develop machine …