ReviewNature reviews. Genetics2021
Decoding disease: from genomes to networks to phenotypes.
Review in Nature reviews. Genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed.
- Spatiotemporal AI-based modeling of gene regulation and variant effects in the developing human brain using Seal.Nature neuroscience · 2026Article
- Reconstructing bovine disease trajectories through integrative multi-omics: molecular decision nodes, predictive biomarkers and precision intervention.Veterinary research communications · 2026Review
- Revisiting retinal and macular degeneration in the genomics era.Nature reviews. Genetics · 2026Review
- Locus-specific stratification and prioritization unveil genetic risk mechanism underlying complex diseases.Nature communications · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Integrative Genomics Identifies Candidate Genes Underlying Trypanotolerance in Hybrid African Cattle.Evolutionary applications · 2026Article
- Cross-species prediction reveals chromatin regions with increased accessibility in humans.Science advances · 2026Article
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Estimating the Genetic Risk of First-Degree Relatives for Chronic Diseases Using the Short Tandem Repeat Score as Model of Polygenic Inheritance.Biochemical genetics · 2025Article
- Integrating natural language processing and genome analysis enables accurate bacterial phenotype prediction.NAR genomics and bioinformatics · 2025Article
- Evaluation and Aggregation of Active Module Identification Algorithms.bioRxiv : the preprint server for biology · 2025Article
- Forward and reverse genomic screens enhance the understanding of phenotypic variation in a large Chinese rhesus macaque cohort.Nature communications · 2025Article
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Unraveling key transposable elements in pathogen-induced bovine mastitis through comparative in vivo and in vitro transcriptomic analysis.BMC genomics · 2025Article
- Progress in multi-omics studies of osteoarthritis.Biomarker research · 2025Review
- Beyond sequence: A physics-informed machine learning framework for predicting DNA mutations.Computational and structural biotechnology journal · 2025Article
- Twenty-first century mouse genetics is again at an inflection point.Lab animal · 2025Review
- Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens.Human genomics · 2024Review
- Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era.World journal of gastroenterology · 2024Review
- A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation.The annals of applied statistics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.
Indexed as
Identifiers
34341555What 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.