ArticleBiomolecules2020
Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 18 citations in OpenAlex.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Pharmacogenomic Drug-Target Network Analysis Reveals Similarity Profiles Among FDA-Approved Cancer Drugs.Pharmaceutics · 2025Article
- Disentangled contrastive learning with dynamic intent adaptation for unveiling gene-drug associations.Briefings in bioinformatics · 2025Article
- A comprehensive insights of cancer immunotherapy resistance.Medical oncology (Northwood, London, England) · 2025Review
- MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Repeat biopsy versus initial biopsy in terms of complication risk factors and clinical outcomes for patients with non-small cell lung cancer: a comparative study of 113 CT-guided needle biopsy of lung lesions.Frontiers in oncology · 2024Article
- Identification of hub genes to determine drug-disease correlation in breast carcinomas.Medical oncology (Northwood, London, England) · 2023Article
- A novel camptothecin derivative, ZBH-01, exhibits superior antitumor efficacy than irinotecan by regulating the cell cycle.Journal of translational medicine · 2023Article
- Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance.Cancers · 2021Review
- Cancer drug resistance induced by EMT: novel therapeutic strategies.Archives of toxicology · 2021Review
- Design, Synthesis, and In Vitro/In Vivo Anti-Cancer Activities of Novel (20International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Cancer is a complex disease affecting millions of people worldwide, with over a hundred clinically approved drugs available. In order to improve therapy, treatment, and response, it is essential to draw better maps of the targets of cancer drugs and possible side interactors. This study presents a large-scale screening method to find associations of cancer drugs with human genes. The analysis is focused on the current collection of Food and Drug Administration (FDA)-approved drugs (which includes about one hundred chemicals). The approach integrates global gene-expression transcriptomic profiles with drug-activity profiles of a set of 60 human cell lines obtained for a collection of chemical compounds (small bioactive molecules). Using a standardized expression for each gene versus standardized activity for each drug, Pearson and Spearman correlations were calculated for all possible pairwise gene-drug combinations. These correlations were used to build a global bipartite network that includes 1007 gene-drug significant associations. The data are integrated into an open web-tool called GEDA (Gene Expression and Drug Activity) which includes a relational view of cancer drugs and genes, disclosing the putative indirect interactions found for FDA-approved drugs as well as the known targets of these drugs. The results also provide insight into the complex action of pharmaceuticals, presenting an alternative view to address predicted pleiotropic effects of the drugs.
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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.