ArticleNature machine intelligence2022
Interpreting Neural Networks for Biological Sequences by Learning Stochastic Masks.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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Who cites it
13 citing papers in PubMed.
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- Prediction of intraductal cancer microinfiltration based on the hierarchical fusion of peri-tumor imaging histology and dual view deep learning.BMC cancer · 2025Article
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.PloS one · 2025Article
- Interpreting cis-regulatory interactions from large-scale deep neural networks.Nature genetics · 2024Article
- Decoding biology with massively parallel reporter assays and machine learning.Genes & development · 2024Review
- Enhancing missense variant pathogenicity prediction with protein language models using VariPred.Scientific reports · 2024Article
- InterpretingbioRxiv : the preprint server for biology · 2024Article
- Functional annotation of enzyme-encoding genes using deep learning with transformer layers.Nature communications · 2023Article
- Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Deciphering the impact of genetic variation on human polyadenylation using APARENT2.Genome biology · 2022Article
- Interpreting Neural Networks for Biological Sequences by Learning Stochastic Masks.Nature machine intelligence · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Sequence-based neural networks can learn to make accurate predictions from large biological datasets, but model interpretation remains challenging. Many existing feature attribution methods are optimized for continuous rather than discrete input patterns and assess individual feature importance in isolation, making them ill-suited for interpreting non-linear interactions in molecular sequences. Building on work in computer vision and natural language processing, we developed an approach based on deep learning - Scrambler networks - wherein the most salient sequence positions are identified with learned input masks. Scramblers learn to predict Position-Specific Scoring Matrices (
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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.