Machine learning meets omics: applications and perspectives

R Li, L Li, Y Xu, J Yang - Briefings in Bioinformatics, 2022 - academic.oup.com
The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …

Machine learning applications for mass spectrometry-based metabolomics

UW Liebal, ANT Phan, M Sudhakar, K Raman… - Metabolites, 2020 - mdpi.com
The metabolome of an organism depends on environmental factors and intracellular
regulation and provides information about the physiological conditions. Metabolomics helps …

Applications of machine learning in metabolomics: Disease modeling and classification

A Galal, M Talal, A Moustafa - Frontiers in genetics, 2022 - frontiersin.org
Metabolomics research has recently gained popularity because it enables the study of
biological traits at the biochemical level and, as a result, can directly reveal what occurs in a …

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

M Sindelar, E Stancliffe, M Schwaiger-Haber… - Cell Reports …, 2021 - cell.com
There is an urgent need to identify which COVID-19 patients will develop life-threatening
illness so that medical resources can be optimally allocated and rapid treatment can be …

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

AK Pakkir Shah, A Walter, F Ottosson, F Russo… - Nature protocols, 2025 - nature.com
Feature-based molecular networking (FBMN) is a popular analysis approach for liquid
chromatography–tandem mass spectrometry-based non-targeted metabolomics data. While …

[HTML][HTML] Deep metabolome: Applications of deep learning in metabolomics

Y Pomyen, K Wanichthanarak, P Poungsombat… - Computational and …, 2020 - Elsevier
In the past few years, deep learning has been successfully applied to various omics data.
However, the applications of deep learning in metabolomics are still relatively low compared …

NMR: unique strengths that enhance modern metabolomics research

AS Edison, M Colonna, GJ Gouveia… - Analytical …, 2020 - ACS Publications
Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in
metabolomics. Because it provides atomic-level detail of small molecules, NMR is …

Interacting quantum atoms—a review

JM Guevara-Vela, E Francisco, T Rocha-Rinza… - Molecules, 2020 - mdpi.com
The aim of this review is threefold. On the one hand, we intend it to serve as a gentle
introduction to the Interacting Quantum Atoms (IQA) methodology for those unfamiliar with it …

Pharmacometabonomics: data processing and statistical analysis

J Fu, Y Zhang, J Liu, X Lian, J Tang… - Briefings in …, 2021 - academic.oup.com
Individual variations in drug efficacy, side effects and adverse drug reactions are still
challenging that cannot be ignored in drug research and development. The aim of …

Impact of feature scaling on machine learning models for the diagnosis of diabetes

DU Ozsahin, MT Mustapha, AS Mubarak… - … in Everything (AIE), 2022 - ieeexplore.ieee.org
Due to its high prevalence and incidence, diabetes is considered significant public health.
Since diabetes has no known cure, early diagnosis plays a vital role in effectively managing …