ArticleJournal of proteome research2023
Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins.
Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation.PLoS computational biology · 2024Pooled it
- Quercetin as a Therapeutic Agent for Oral Cancer: Current Evidence and Future Directions.Asia-Pacific journal of clinical oncology · 2026Review
- Distinct Functional Signatures of Human Olfactory and Respiratory Mucus Revealed by Proteomics Combined With Machine Learning.Proteomics. Clinical applications · 2026Article
- MLMarker: a machine learning framework for tissue inference and biomarker discovery.Genome biology · 2026Article
- Article
- Review
- Potential of Proteomics in Forensic Phenotyping: A Focus on Biological Sex Estimation.Journal of proteome research · 2025Article
- Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis.Heliyon · 2024Article
- Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery.Journal of proteome research · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.
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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.