ArticleMolecular & cellular proteomics : MCP2015
Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.
Article in Molecular & cellular proteomics : MCP, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
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- Serum Proteomic Profiling to Identify Biomarkers of Premature Carotid Atherosclerosis.Scientific reports · 2018Trial
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
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- Proteomic analysis of metabolic adaptation in a unicellular cyanobacterium during light-dark cycles and nitrogen fixation.Scientific reports · 2025Article
- Computational modelling of aggressive B-cell lymphoma.Biochemical Society transactions · 2025Review
- Zinc-mediated dynamics of CD4/CD8α co-receptors and Lck kinase: implications for zinc homeostasis, immune response, and biotechnological innovations.Metallomics : integrated biometal science · 2025Review
- SP140 represses specific loci by recruiting polycomb repressive complex 2 and NuRD complex.Nucleic acids research · 2025Article
- Proteomic changes orchestrate metabolic acclimation of a unicellular diazotrophic cyanobacterium during light-dark cycle and nitrogen fixation states.bioRxiv : the preprint server for biology · 2024Article
- Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease.Biomedical engineering letters · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma.Molecular & cellular proteomics : MCP · 2023Article
- The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.Journal of personalized medicine · 2022Review
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- The serum of COVID-19 asymptomatic patients up-regulates proteins related to endothelial dysfunction and viral response in circulating angiogenic cells ex-vivo.Molecular medicine (Cambridge, Mass.) · 2022Article
- Towards Precision Dermatology: Emerging Role of Proteomic Analysis of the Skin.Dermatology (Basel, Switzerland) · 2022Review
- The promise of organoids for unraveling the proteomic landscape of the developing human brain.Molecular psychiatry · 2022Review
- Phosphoproteomics and Organelle Proteomics in Pancreatic Islets.Methods in molecular biology (Clifton, N.J.) · 2022Article
- LC-MS peak assignment based on unanimous selection by six machine learning algorithms.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77-89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.
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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.