ArticleScientific reports2024
Reliable anti-cancer drug sensitivity prediction and prioritization.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.PLoS computational biology · 2026Article
- Leveraging uncertainty estimates for drug response prediction in cancer cell lines.Bioinformatics advances · 2026Article
- Article
- Reliable machine learning models in genomic medicine using conformal prediction.Frontiers in bioinformatics · 2025Review
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning (ML) to solve real-world problems does not only bear great potential but also high risk. One fundamental challenge in risk mitigation is to ensure the reliability of the ML predictions, i.e., the model error should be minimized, and the prediction uncertainty should be estimated. Especially for medical applications, the importance of reliable predictions can not be understated. Here, we address this challenge for anti-cancer drug sensitivity prediction and prioritization. To this end, we present a novel drug sensitivity prediction and prioritization approach guaranteeing user-specified certainty levels. The developed conformal prediction approach is applicable to classification, regression, and simultaneous regression and classification. Additionally, we propose a novel drug sensitivity measure that is based on clinically relevant drug concentrations and enables a straightforward prioritization of drugs for a given cancer sample.
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Registered trials
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