aiSysMet: AI-Powered Systems Metabolomics for Biomarker Discovery

Clicks: 1
ID: 321320
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #801 of 818 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 818 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Metabolomics plays an essential role in the growing systems biology approaches to unravel the relationships between metabolites and diseases. Liquid chromatography-mass spectrometry (LC-MS) is central to this effort because it can profile many metabolites from limited material. Yet, in a typical untargeted LC-MS-based metabolomics study, the majority of detected peaks remain unannotated, largely due to incomplete spectral libraries and uncertainties in peak picking, alignment, and the handling of isotopes and adducts. These limitations hinder seamless integration with other omics layers. RESULTS: We developed an AI-powered platform (aiSysMet) that uses statistical, machine learning, and deep learning methods for metabolomics data processing, metabolite annotation, and integrative analysis of multi-omics data. The platform's interactive and modular web interface allows users to easily build data analysis pipelines that can be executed in the cloud. AVAILABILITY: aiSysMet is freely available for non-commercial users on https://tools.omicscraft.com/aiSysMet.
Reference Key
openalex_W7168669958 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Habtom Ressom, 延林 戈, Hongyu Ao, Xinran Zhang, Sara Hashemi, Rency Varghese, Bardia Nezami, Dawit Mengistu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag520
URL
https://doi.org/10.1093/bioinformatics/btag520
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.