ArticleScientific reports2018
A novel heterogeneous network-based method for drug response prediction in cancer cell lines.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- DNA Repair Genes as Drug Candidates for Early Breast Cancer Onset in Latin America: A Systematic Review.International journal of molecular sciences · 2021Pooled it
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- QAL333's antitumor activity and predictive modeling: integrated transcriptomic-bioinformatic analysis reveals selective cytotoxicity and sensitivity determinants.Scientific reports · 2026Article
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- DVFGCDR: a dual-view fusion graph neural network for cancer drug response prediction.NAR cancer · 2025Article
- A network model for patient-derived drug response in breast cancer integrating multi-omics datasets.bioRxiv : the preprint server for biology · 2025Article
- MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.BMC bioinformatics · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.PLoS computational biology · 2025Article
- 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
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma.Heliyon · 2024Article
- Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.PloS one · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Toward Unified AI Drug Discovery with Multimodal Knowledge.Health data science · 2024Article
- MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.Bioinformatics (Oxford, England) · 2023Article
- A subcomponent-guided deep learning method for interpretable cancer drug response prediction.PLoS computational biology · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP .
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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.