ArticleCancer medicine2025
Machine Learning-Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Integrative Transcriptomic and Machine Learning Analysis of ecDNA-Associated Features for Studying Chemotherapy Resistance in TNBC.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning-Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity.Cancer medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundPrecision medicine has personalized anticancer therapies and has been considered standard practice. Although current cancer genomic profiling tests are powerful tools to predict the efficacy of molecular targeted drugs or immune checkpoint inhibitors, they are not readily applicable for classical anticancer agents. In this study, we report a novel concept of phenotype-based classification using machine learning analysis of gene expression patterns to predict the effectiveness of anticancer agents.
methodsHierarchical clustering of IC
resultsCisplatin-resistant A549CR cells experimentally confirmed the external validity of the CSP26G model. The model also classified patients with non-small cell lung cancer in The Cancer Genome Atlas (TCGA) clinical database into cisplatin-sensitive and cisplatin-resistant groups. The predicted sensitive group showed significantly longer survival than the predicted resistant group. Furthermore, CSP26G predicts not only cisplatin efficacy but also responsiveness to other DNA-damaging agents.
conclusionThese findings indicate that the sensitivity prediction model constructed through the integration of DEG and machine learning analyses can forecast drug sensitivity, thereby contributing to the advancement of effective and personalized precision medicine in classical chemotherapies.
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Registered trials
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