ArticleBMC medical genomics2020
Network-based drug sensitivity prediction.
Article in BMC medical genomics, 2020. 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.
- Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model.Bioinformatics (Oxford, England) · 2026Article
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- Network-based multi-class classifier to identify optimized gene networks for acute leukemia cell line classification.PloS one · 2025Article
- Generalized information criteria for personalized gene network inference.Frontiers in genetics · 2025Article
- Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.PloS one · 2025Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 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
- Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.Briefings in bioinformatics · 2024Article
- GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network.Bioinformatics (Oxford, England) · 2024Article
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
- Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.Communications biology · 2023Article
- Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.Heliyon · 2023Article
- Article
- Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.Frontiers in medicine · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Computational Tactics for Precision Cancer Network Biology.International journal of molecular sciences · 2022Review
- omicsGAT: Graph Attention Network for Cancer Subtype Analyses.International journal of molecular sciences · 2022Article
- CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation.Bioinformatics (Oxford, England) · 2022Article
- Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.International journal of molecular sciences · 2022Article
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6 authors.
Funding
Abstract
backgroundDrug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study.
methodsIn this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available at https://github.com/compbiolabucf/drug-sensitivity-prediction .
resultsIn the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug's mechanism of action.
conclusionsNetwork-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets.
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