ArticleBriefings in bioinformatics2020
Deep learning of pharmacogenomics resources: moving towards precision oncology.
Article in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 75 citations in OpenAlex.
- Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.Journal of personalized medicine · 2026Review
- Optimizing computational prediction of shrimp-WSSV protein interactions through integration of homology- and topology-based predictions.Molecular genetics and genomics : MGG · 2026Article
- A deep learning model for the diagnosis of gastric neuroendocrine carcinoma.Communications medicine · 2026Article
- Comparative Analysis of Generative AI Language Models in Orthodontics: Evidence-Based Insights Into Perplexity, iASK, and ChatGPT 4o Mini.TheScientificWorldJournal · 2026Article
- DrugSurvPlot: A Novel Web-Based Platform Harnessing Drug Sensitivity Scores as Molecular Biomarkers for Pan-Cancer Survival Prognosis.Current gene therapy · 2026Article
- Genome wide landscaping of copy number variations for horse inter-breed variability.Animal biotechnology · 2025Article
- Cancer detection via one-shot learning: integrating gene expression and genomic mutation analysis.BMC bioinformatics · 2025Article
- A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenografts.NAR genomics and bioinformatics · 2025Article
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction.Frontiers in genetics · 2025Article
- Review
- Predicting drug response through tumor deconvolution by cancer cell lines.Patterns (New York, N.Y.) · 2024Article
- Improving drug response prediction via integrating gene relationships with deep learning.Briefings in bioinformatics · 2024Article
- shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.Patterns (New York, N.Y.) · 2024Article
- Article
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
- Recent advancement in targeted therapy and role of emerging technologies to treat cancer.Medical oncology (Northwood, London, England) · 2023Review
- LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.Interdisciplinary sciences, computational life sciences · 2023Article
- CREAMMIST: an integrative probabilistic database for cancer drug response prediction.Nucleic acids research · 2023Article
- Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition.BioMed research international · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.
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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.