ArticleScientific reports2022
Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.Journal of computer-aided molecular design · 2026Article
- Identification and validation of aryl hydrocarbon receptor-associated hub genes in ulcerative colitis via integrated bioinformatics analysis.Human genomics · 2025Article
- Article
- A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.Biomolecules · 2024Review
- 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
- Ligand-Based Compound Activity Prediction via Few-Shot Learning.Journal of chemical information and modeling · 2024Article
- Reliable anti-cancer drug sensitivity prediction and prioritization.Scientific reports · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors.Biosensors · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone.
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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.