ArticleScientific reports2019
Modeling cancer drug response through drug-specific informative genes.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 142 citations in OpenAlex.
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- Leveraging drug-specific genes to identify sensitizers for resistant cancer cell lines.Cell death discovery · 2026Article
- Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study.Journal of advanced research · 2026Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Comparative evaluation of feature reduction methods for drug response prediction.Scientific reports · 2024Article
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- Learning chemical sensitivity reveals mechanisms of cellular response.Communications biology · 2024Article
- TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.Genomics, proteomics & bioinformatics · 2024Article
- Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma.Heliyon · 2024Article
- A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.Cancers · 2024Article
- Differential drug response in pulmonary arterial hypertension: The potential for precision medicine.Pulmonary circulation · 2023Review
- Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.Heliyon · 2023Article
- MAPK inhibitor sensitivity scores predict sensitivity driven by the immune infiltration in pediatric low-grade gliomas.Nature communications · 2023Article
- A performance evaluation of drug response prediction models for individual drugs.Scientific reports · 2023Article
- Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.BMC bioinformatics · 2023Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.Frontiers in molecular biosciences · 2023Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and predict drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets.
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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.