ReviewOmics : a journal of integrative biology2013
Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology.
Review in Omics : a journal of integrative biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers.
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
112 citing papers in PubMed.
- Proteomics in Gastrointestinal Diseases of Companion Animals: Current State and Knowledge Gaps.Animals : an open access journal from MDPI · 2026Review
- Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.Metabolites · 2026Review
- The Use of DeepQSAR Models for the Discovery of Peptides With Enhanced Antimicrobial and Antibiofilm Potential.Molecular informatics · 2026Article
- Bioinformatic Screening of Key Regulatory Molecules and Mechanisms in Osteoporosis Based on Tryptophan Metabolism-Related Genes.International journal of general medicine · 2026Article
- Biomarker identification through spatial proteomics for the characterization of indeterminate thyroid nodules.Endocrine · 2025Article
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- Preliminary screening of urinary host protein biomarkers for Schistosomiasis haematobium: A proteome profiling study identifying candidate diagnostic targets in school-aged children.PLoS neglected tropical diseases · 2025Article
- A Comprehensive Proteome of Human Corneal Epithelial Cells Constructed by Cross-platform DIA-Mass Spectrometry.Scientific data · 2025Article
- Clinical Features and Plasma Metabolites Analysis in Obese Chinese Children With Nonalcoholic Fatty Liver Disease.Journal of the Endocrine Society · 2025Article
- Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis.Heliyon · 2024Article
- Integrating genetic algorithms and language models for enhanced enzyme design.Briefings in bioinformatics · 2024Article
- Review
- Mass Spectrometry Advancements and Applications for Biomarker Discovery, Diagnostic Innovations, and Personalized Medicine.International journal of molecular sciences · 2024Review
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- Proteomics of the heart.Physiological reviews · 2024Review
- Multiscale engineering of brain organoids for disease modeling.Advanced drug delivery reviews · 2024Review
- SNP Array for Small-Shrimp (GenusFoods (Basel, Switzerland) · 2024Article
- Enhancing selection of alcohol consumption-associated genes by random forest.The British journal of nutrition · 2024Article
- Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.Clinical proteomics · 2024Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.
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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.