ArticleBriefings in bioinformatics2024
Improving drug response prediction via integrating gene relationships with deep learning.
Article in Briefings in bioinformatics, 2024. 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, 26 citations in OpenAlex.
- Expression quantitative trait methylation across multiple cancer types with functional and therapeutic characterization using Onco-eQTM.NAR genomics and bioinformatics · 2026Article
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Critical evaluation of drug response prediction models with DrEval.Nature communications · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- The Current State and Future Prospects for Artificial Intelligence in Dermatology.Dermatologic clinics · 2025Review
- Multiomics-Driven Drug-Cell Interaction Network for Chemotherapy Sensitivity Prediction in Metabolically Defined Triple-Negative Breast Cancer Subtypes.Journal of cellular and molecular medicine · 2025Article
- Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease.Alzheimer's research & therapy · 2025Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.
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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.