ReviewBriefings in bioinformatics2021
Interpretation of deep learning in genomics and epigenomics.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
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- Personalizing Antidepressant Therapy: Integrating Pharmacogenomics, Therapeutic Drug Monitoring, and Digital Tools for Improved Depression Outcomes.Journal of personalized medicine · 2025Review
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Multiple Strategies Confirm the Anti Hepatocellular Carcinoma Effect of Cinnamic Acid Based on the PI3k-AKT Pathway.Pharmaceuticals (Basel, Switzerland) · 2025Article
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- Multi-task genomic prediction using gated residual variable selection neural networks.BMC bioinformatics · 2025Article
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Adversarial attack of sequence-free enhancer prediction identifies chromatin architecture.Bioinformatics (Oxford, England) · 2025Article
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation.Nature genetics · 2025Article
- Current Progress and Future Trends of Genomics-Based Techniques for Food Adulteration Identification.Foods (Basel, Switzerland) · 2025Review
- Precise engineering of gene expression by editing plasticity.Genome biology · 2025Article
- scHiGex: predicting single-cell gene expression based on single-cell Hi-C data.NAR genomics and bioinformatics · 2025Article
- An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis.IEEE transactions on medical imaging · 2025Article
7 more citing papers are in PubMed but not listed here.
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
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.
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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.