ArticleInternational journal of molecular sciences2022
Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 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
- Structure-Property Associations: Breaking Paradigms for Linking Chemical Structures and Biological Properties in Drug Discovery.ChemMedChem · 2026Article
- Molecular Pharmacology at the Crossroads of Precision Medicine.Current issues in molecular biology · 2025Article
- Multi-Omics and Functional Analysis of BFSP1 as a Prognostic and Therapeutic Target in Liver Hepatocellular Carcinoma.Medicina (Kaunas, Lithuania) · 2025Article
- VPS26A as a Prognostic Biomarker and Therapeutic Target in Liver Hepatocellular Carcinoma: Insights from Comprehensive Bioinformatics Analysis.Medicina (Kaunas, Lithuania) · 2025Article
- Predicting time-to-first cancer diagnosis across multiple cancer types.Scientific reports · 2025Article
- Establishing predictive machine learning models for drug responses in patient derived cell culture.NPJ precision oncology · 2025Article
- Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction.Scientific reports · 2024Article
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- The Fault Diagnosis of Rolling Bearings Is Conducted by Employing a Dual-Branch Convolutional Capsule Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Functional transcriptional signatures for tumor-type-agnostic phenotype prediction.bioRxiv : the preprint server for biology · 2024Article
- Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.JCO clinical cancer informatics · 2023Article
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 2023Article
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".International journal of molecular sciences · 2022Article
- Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.
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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.