ArticleScientific reports2019
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 4 citations in OpenAlex.
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- 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
- 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
- Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine.Chinese medicine · 2023Review
- Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.Bioinformatics (Oxford, England) · 2023Article
- Machine learning approaches to predict drug efficacy and toxicity in oncology.Cell reports methods · 2023Review
- Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.Frontiers in bioengineering and biotechnology · 2023Article
- Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.Scientific reports · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis.BioData mining · 2021Article
- In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses.International journal of molecular sciences · 2021Article
- Article
- Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.BMC bioinformatics · 2020Article
- PaccMann: a web service for interpretable anticancer compound sensitivity prediction.Nucleic acids research · 2020Article
- Computational Model Reveals a Stochastic Mechanism behind Germinal Center Clonal Bursts.Cells · 2020Article
- Incorporating biological structure into machine learning models in biomedicine.Current opinion in biotechnology · 2020Review
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4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bias weight distribution. The bias weight distribution is obtained from the assignment of high weights to the drug targets and propagating the assigned weights over a protein-protein interaction network such as STRING. The propagation of weights, defines neighborhoods of influence around the drug targets and as such simulates the spread of perturbations within the cell, following drug administration. Using a synthetic dataset, we showcase how application of biased tree ensembles (BiTE) results in significant accuracy gains at a much lower computational cost compared to the unbiased random forests (RF) algorithm. We then apply NetBiTE to the Genomics of Drug Sensitivity in Cancer (GDSC) dataset and demonstrate that NetBiTE outperforms RF in predicting IC50 drug sensitivity, only for drugs that target membrane receptor pathways (MRPs): RTK, EGFR and IGFR signaling pathways. We propose based on the NetBiTE results, that for drugs that inhibit MRPs, the expression of target genes prior to drug administration is a biomarker for IC50 drug sensitivity following drug administration. We further verify and reinforce this proposition through control studies on, PI3K/MTOR signaling pathway inhibitors, a drug category that does not target MRPs, and through assignment of dummy targets to MRP inhibiting drugs and investigating the variation in NetBiTE accuracy.
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