ArticleComputational and structural biotechnology journal2023
A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Integrated metabolome and transcriptome analysis reveals ferroptosis involvement in cisplatin resistance of esophageal squamous cancer cell.Computational and structural biotechnology journal · 2026Article
- Machine Learning-Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity.Cancer medicine · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Overexpressed NEK2 contributes to progression and cisplatin resistance through activating the Wnt/β-catenin signaling pathway in cervical cancer.Cancer cell international · 2025Article
- AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data.Patterns (New York, N.Y.) · 2024Article
- PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia.Blood cancer journal · 2023Article
- A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia.International journal of molecular sciences · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting.
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Registered trials
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