ReviewBriefings in bioinformatics2017
Computational models for predicting drug responses in cancer research.
Review in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
87 citing papers in PubMed.
- A Pharmacogenetic Prediction Model of Progression-Free Survival in Breast Cancer using Genome-Wide Genotyping Data from CALGB 40502 (Alliance).Clinical pharmacology and therapeutics · 2019Trial
- Integrative multi-omics profiling and machine learning reveal enhancer RNA signatures for early detection and prognostic stratification in breast cancer.NAR cancer · 2026Article
- DeepDisSNP: Predicting disease-associated SNPs by representation learning on disease and SNP linkage disequilibrium networks.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- GenReP: An Ensemble Model for Predicting TP53 in Response to Pharmaceutical Compounds.Molecules (Basel, Switzerland) · 2026Article
- DisSNPNet: Predicting disease-associated single-nucleotide polymorphisms using linkage disequilibrium, disease similarity, and 1000 Genomes Project datasets with evidence-based validation.Biology methods & protocols · 2026Article
- Article
- Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.Clinical and experimental medicine · 2025Review
- Review
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
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- Advanced Technologies for Large Scale Supply of Marine Drugs.Marine drugs · 2025Review
- The impact of compression and confinement in tumor growth and progression: emerging concepts in cancer mechanobiology.Frontiers in materials · 2025Article
- MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.Journal of integrative bioinformatics · 2024Article
- Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data.Expert opinion on drug discovery · 2024Review
- An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery.Journal of translational medicine · 2024Article
- Cancer drug sensitivity prediction from routine histology images.NPJ precision oncology · 2024Article
- Bactericidal, anti-hemolytic, and anticancerous activities of phytofabricated silver nanoparticles of glycine max seeds.Frontiers in chemistry · 2024Article
- Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in 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.