ReviewBriefings in bioinformatics2024
Designing interpretable deep learning applications for functional genomics: a quantitative analysis.
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, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Positional interpretation of cis-regulatory code and nucleosome organization with deep learning models.Nature communications · 2026Article
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- Systems biology in the era of AI: "winter" or "evolution"?Frontiers in systems biology · 2026Article
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- ACmix-Swin Deep Learning of 4-Day-OldGenes · 2025Article
- Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.Annals of medicine · 2025Article
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.Biomedicines · 2025Review
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Fine-tuning protein language models to understand the functional impact of missense variants.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can be challenging. Interpretability is essential not only to help progress our understanding of the biological mechanisms underlying traits and diseases but also for establishing trust in these model's efficacy for healthcare applications. Recognizing this importance, recent years have seen the development of numerous diverse interpretability strategies, making it increasingly difficult to navigate the field. In this review, we present a quantitative analysis of the challenges arising when designing interpretable deep learning solutions in functional genomics. We explore design choices related to the characteristics of genomics data, the neural network architectures applied, and strategies for interpretation. By quantifying the current state of the field with a predefined set of criteria, we find the most frequent solutions, highlight exceptional examples, and identify unexplored opportunities for developing interpretable deep learning models in genomics.
Indexed as
Identifiers
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.