ArticleGenome biology2023
ExplaiNN: interpretable and transparent neural networks for genomics.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.Biochemical genetics · 2026Article
- An atlas of transcription factor cooperation reveals how motif readers shape regulatory output.bioRxiv : the preprint server for biology · 2026Article
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- scKanFormer: A Transformer-KAN framework with biologically informed attention for cell type annotation in large-scale scRNA-seq data.PLoS computational biology · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.PLoS genetics · 2026Article
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
- JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models.Nucleic acids research · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- gReLU: a comprehensive framework for DNA sequence modeling and design.Nature methods · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- A Biologically Informed and Efficient DNA Sequence Learner for Predicting Functional Genomics Events.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- Environmental adaptations in metagenomes revealed by deep learning.BMC biology · 2025Article
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 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
5 authors.
Funding
Abstract
Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motifs, achieving performance comparable to state-of-the-art methods. Its predictions are transparent, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. ExplaiNN can serve as a plug-and-play platform for pretrained models and annotated position weight matrices. ExplaiNN aims to accelerate the adoption of deep learning in genomic sequence analysis by domain experts.
Indexed as
Identifiers
What Socratic 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 Socratic graph.