ArticleBMC cancer2021
Predicting breast cancer drug response using a multiple-layer cell line drug response network model.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- A Systematic Machine Learning Framework for Evaluating and Ranking Omics Layers in Cancer Drug Response Prediction.Current issues in molecular biology · 2026Article
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).Biomedical reports · 2025Review
- Exploiting similarity in drug molecular effects for drug repurposing.Human genomics · 2025Article
- ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.PLoS computational biology · 2025Article
- Predicting drug response through tumor deconvolution by cancer cell lines.Patterns (New York, N.Y.) · 2024Article
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.PLOS digital health · 2023Article
- XMR: an explainable multimodal neural network for drug response prediction.Frontiers in bioinformatics · 2023Article
- Artificial Intellgence in the Era of Precision Oncological Imaging.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting patient drug response based on a patient's molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs.
methodsWe first collected the baseline gene expression profiles of 49 BC cell lines along with IC
resultsML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC
conclusionsThe ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.
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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.