ArticleScientific reports2017
Heterogeneity Aware Random Forest for Drug Sensitivity Prediction.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed.
- Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis.Cancer medicine · 2023Trial
- The intersection of disorders of sex development and cardiovascular diseases.Biology of sex differences · 2026Review
- Laminin-gamma2 promotes oral squamous cell carcinoma growth by activating the p-ERK/c-Myc signaling pathway.BMC oral health · 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
- 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
- Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.Cancers · 2023Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.Molecular diversity · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.Scientific reports · 2022Article
- Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
- Machine Learning-Based Prediction Method for Tremors Induced by Tacrolimus in the Treatment of Nephrotic Syndrome.Frontiers in pharmacology · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- [Prediction of intensive care unit readmission for critically ill patients based on ensemble learning].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2021Article
- A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests.Genes · 2021Article
- Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification.Cancers · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogeneity Aware Random Forests (HARF) that assigns weights to the trees based on the category of the sample. We treat heterogeneity as a latent class allocation problem and present a covariate free class allocation approach based on the distribution of leaf nodes of the model ensemble. Applications on CCLE and GDSC databases show that HARF outperforms traditional Random Forest when the average drug responses of cancer types are different.
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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.