ReviewBriefings in bioinformatics2024
Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.Biochemical genetics · 2026Article
- Non-Coding Negative Regulatory Features in Livestock Genomes: Functional Annotation and Causal Validation.Animals : an open access journal from MDPI · 2026Review
- Review
- Investigation of cervical cell image segmentation technology based on deep learning and non-coding RNAs.Non-coding RNA research · 2026Article
- Cis-regulatory and long noncoding RNA alterations in breast cancer - current insights, biomarker utility, and the critical need for functional validation.Molecular oncology · 2026Review
- Genetic Architecture of N-Terminal Pro-B-Type Natriuretic Peptide in a Multiancestry Study Population.Circulation. Genomic and precision medicine · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Exploring the roles of conserved context-dependent cis-regulatory elements (cdCREs) in multicellularity, human health and disease.The FEBS journal · 2025Review
- Review
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Non-coding RNA-mediated gene regulation in Alzheimer's disease pathogenesis: molecular insights and emerging innovations.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Low-frequency genetic variants in GAK enhance Golgi function and protect against Parkinson's disease.medRxiv : the preprint server for health sciences · 2025Article
- Epigenetic Impacts of Non-Coding Mutations Deciphered Through Pre-Trained DNA Language Model at Single-Cell Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft.Human mutation · 2025Review
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant 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.