ArticleBMC biology2024
TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion.
Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- RLASON-CDR: a reinforcement learning-driven adaptive synergistic optimization network for cancer drug response prediction.Briefings in bioinformatics · 2026Article
- Drug response profile-based machine learning enables strategic cell line and compound selection for drug development.Bioinformatics (Oxford, England) · 2026Article
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AMDRP: adaptive drug feature fusion and multihead bidirectional cross-attention network for drug-cancer cell response prediction.Molecular diversity · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- Review
- Decoding the role of cancer stem cells in digestive tract tumors: Mechanisms and therapeutic implications (Review).International journal of oncology · 2025Review
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines.
resultsWe introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC
conclusionsTransCDR emerges as a potent tool with significant potential in drug response prediction.
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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 Socratic graph.