ArticleNature machine intelligence2024
Interpreting
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Functional motif detection via in silico ablation using AlphaGenome.Briefings in bioinformatics · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- PoolParty: streamlined design of DNA sequence libraries in Python.bioRxiv : the preprint server for biology · 2026Article
- Genetic background shapes AI-predicted variant effects.bioRxiv : the preprint server for biology · 2026Article
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- D-LIM: A neural network for interpretable gene-gene interactions.PLoS computational biology · 2026Article
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- Uncovering the Mechanistic Landscape of Regulatory DNA with Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Evaluating the representational power of pre-trained DNA language models for regulatory genomics.Genome biology · 2025Article
- ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants.bioRxiv : the preprint server for biology · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
Corrections and comments
- Update ofInterpreting2024
Authors and funding
4 authors.
Funding
Abstract
Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. However, elucidating underlying biological mechanisms from genomic DNNs remains challenging. Existing interpretability methods, such as attribution maps, have their origins in non-biological machine learning applications and therefore have the potential to be improved by incorporating domain-specific interpretation strategies. Here we introduce SQUID, a genomic DNN interpretability framework based on domain-specific surrogate modeling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models-simpler quantitative models that have inherently interpretable mathematical forms. SQUID leverages domain knowledge to model
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.