ArticlePloS one2021
Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- DrugSurvPlot: A Novel Web-Based Platform Harnessing Drug Sensitivity Scores as Molecular Biomarkers for Pan-Cancer Survival Prognosis.Current gene therapy · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data.Scientific reports · 2025Article
- Predicting progression events in multiple myeloma from routine blood work.NPJ digital medicine · 2025Article
- Review
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
- Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach.International journal of molecular sciences · 2023Article
- New Developments in Myeloma Treatment and Response Assessment.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myeloma.NPJ precision oncology · 2023Article
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- Kinase Inhibition in Multiple Myeloma: Current Scenario and Clinical Perspectives.Pharmaceutics · 2022Review
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line.
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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.