ArticleFrontiers in genetics2018
Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 15 citations in OpenAlex.
- Trial
- Target-biology and interactome-derived signatures predict target-level associations with safety-related drug attrition.Scientific reports · 2026Article
- Drug toxicity prediction based on genotype-phenotype differences between preclinical models and humans.EBioMedicine · 2025Article
- Variant-based heritability assessment of dexmedetomidine and fentanyl clearance in pediatric patients.Clinical and translational science · 2023Article
- An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.PLOS digital health · 2022Article
- Tetrabromobisphenol A effects on differentiating mouse embryonic stem cells reveals unexpected impact on immune system.Frontiers in genetics · 2022Article
- Exploration of the DARTable Genome- a Resource Enabling Data-Driven NAMs for Developmental and Reproductive Toxicity Prediction.Frontiers in toxicology · 2021Article
- An omics perspective on drug target discovery platforms.Briefings in bioinformatics · 2020Article
- Unlocking the potential ofComputational toxicology (Amsterdam, Netherlands) · 2019Article
- Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury.Frontiers in genetics · 2018Article
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 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.
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What OpenQuestion holds
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.