ArticleFrontiers in genetics2019
Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A Knowledge-Enhanced Multimodal Framework with Genomic Reconstruction for DLBCL Drug Response Prediction.Computational and structural biotechnology journal · 2026Article
- Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets.Scientific reports · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.Healthcare technology letters · 2024Article
- A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.Functional & integrative genomics · 2024Review
- PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.Journal of cellular and molecular medicine · 2024Article
- A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.Heliyon · 2024Article
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
- Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction.PeerJ. Computer science · 2024Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.Frontiers in genetics · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.PLoS computational biology · 2022Article
- Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Dissecting the Genome for Drug Response Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.Biomedicines · 2021Article
- Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anticancer drug responses can be varied for individual patients. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to construct classification models to predict the drug response. This research focuses on the feature selection issue for the classification models. Because of the vast dimensions of the feature space for predicting drug response, the autoencoder network was first built, and a subset of inputs with the important contribution was selected. Then by using the Boruta algorithm, a further small set of features was determined for the random forest, which was used to predict drug response. Two datasets, GDSC and CCLE, were used to illustrate the efficiency of the proposed method.
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