ArticleNucleic acids research2021
A self-attention model for inferring cooperativity between regulatory features.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.Cell genomics · 2026Article
- A comprehensive evaluation of self-attention for detecting regulatory feature interactions.NAR genomics and bioinformatics · 2026Article
- Deep Learning-Based Identification of Pathogenicity Genes inPlants (Basel, Switzerland) · 2026Article
- Massive experimental quantification allows interpretable deep learning of protein aggregation.Science advances · 2025Article
- Cropformer: An interpretable deep learning framework for crop genomic prediction.Plant communications · 2025Article
- Massive experimental quantification of amyloid nucleation allows interpretable deep learning of protein aggregation.bioRxiv : the preprint server for biology · 2024Article
- TIANA: transcription factors cooperativity inference analysis with neural attention.BMC bioinformatics · 2024Article
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.Nucleic acids research · 2024Article
- Predictive analyses of regulatory sequences with EUGENe.Nature computational science · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.Nucleic acids research · 2023Article
- Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention.Genome biology · 2023Article
- RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.Briefings in bioinformatics · 2023Article
- CRMnet: A deep learning model for predicting gene expression from large regulatory sequence datasets.Frontiers in big data · 2023Article
- Article
- omicsGAT: Graph Attention Network for Cancer Subtype Analyses.International journal of molecular sciences · 2022Article
- Characterizing collaborative transcription regulation with a graph-based deep learning approach.PLoS computational biology · 2022Article
- Article
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2 authors.
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Abstract
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.
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