ArticleIEEE journal of biomedical and health informatics2016
Integrative Analysis of Proteomic, Glycomic, and Metabolomic Data for Biomarker Discovery.
Article in IEEE journal of biomedical and health informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Multi-model applications and cutting-edge advancements of artificial intelligence in hepatology in the era of precision medicine.World journal of gastroenterology · 2025Review
- Multi-Omics Feature Selection to Identify Biomarkers for Hepatocellular Carcinoma.Metabolites · 2025Article
- Metabolomic and Proteomic Profiling of Athletes Performing Physical Activity under Hypoxic Conditions.Sports (Basel, Switzerland) · 2024Article
- Applying genetic technologies to combat infectious diseases in aquaculture.Reviews in aquaculture · 2023Review
- Article
- Incorporating Omics Data in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
- MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery.Metabolites · 2020Article
- Multi-omic Pathway and Network Analysis to Identify Biomarkers for Hepatocellular Carcinoma.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Studies associating changes in the levels of multiple biomolecules including proteins, glycans, glycoproteins, and metabolites with the onset of cancer have been widely investigated to identify clinically relevant diagnostic biomarkers. Advances in liquid or gas chromatography mass spectrometry (LC-MS, GC-MS) have enabled high-throughput qualitative and quantitative analysis of these biomolecules. While results from separate analyses of different biomolecules have been reported widely, the mutual information obtained by partly or fully combining them has been relatively unexplored. In this study, we investigate integrative analysis of proteins, N-glycans, and metabolites to take advantage of complementary information to improve the ability to distinguish cancer cases from controls. Specifically, support vector machine-recursive feature elimination algorithm is utilized to select a panel of proteins, N-glycans, and metabolites based on LC-MS and GC-MS data previously acquired by the analysis of blood samples from two cohorts in a liver cancer study. Improved performances are observed by integrative analysis compared to separate proteomic, glycomic, and metabolomic studies in distinguishing liver cancer cases from patients with liver cirrhosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.