ArticleAnnual review of biomedical data science2018
Big Data Approaches for Modeling Response and Resistance to Cancer Drugs.
Article in Annual review of biomedical data science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative Bioinformatics Analysis: Unraveling Variant Signatures and Single-Nucleotide Polymorphism Markers Associated with 5-FU-Based Chemotherapy Resistance in Colorectal Cancer Patients.Journal of gastrointestinal cancer · 2024Pooled it
- Article
- The present and future of the Cancer Dependency Map.Nature reviews. Cancer · 2025Review
- Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL.Genomics, proteomics & bioinformatics · 2023Article
- Big data in basic and translational cancer research.Nature reviews. Cancer · 2022Review
- Systematic investigation of cytokine signaling activity at the tissue and single-cell levels.Nature methods · 2021Article
- Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anticancer drug response.NAR genomics and bioinformatics · 2021Article
- Systematic prediction of drug resistance caused by transporter genes in cancer cells.Scientific reports · 2021Article
- The promise and challenge of cancer microbiome research.Genome biology · 2020Review
- Tissue-guided LASSO for prediction of clinical drug response using preclinical samples.PLoS computational biology · 2020Article
- Use of big data in drug development for precision medicine: an update.Expert review of precision medicine and drug development · 2019Article
- pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma.Communications biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.
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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.