ArticleBriefings in bioinformatics2021
Explainability in transformer models for functional genomics.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 57 citations in OpenAlex.
- PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.Science advances · 2026Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification.Scientific reports · 2026Article
- PromoterAtlas: decoding regulatory sequences across Gammaproteobacteria using a transformer model.Nature communications · 2026Article
- Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.Nature communications · 2026Article
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- A Self-Supervised Pre-Trained Transformer Model for Accurate Genomic Prediction of Swine Phenotypes.Animals : an open access journal from MDPI · 2025Article
- Explainable AI-Based Feature Selection Approaches for Raman Spectroscopy.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from TNPJ digital medicine · 2025Article
- Evaluating the representational power of pre-trained DNA language models for regulatory genomics.Genome biology · 2025Article
- GENA-LM: a family of open-source foundational DNA language models for long sequences.Nucleic acids research · 2025Article
- DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.Scientific reports · 2025Article
- DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.Scientific reports · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Deep Genomics: Deep Learning-Based Analysis of Genome-Sequenced Data for Identification of Gene Alterations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Evaluating the representational power of pre-trained DNA language models for regulatory genomics.bioRxiv : the preprint server for biology · 2024Article
- Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.NPJ precision oncology · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs from DNA sequences. To benefit from automated learning methods, new strategies are required that unveil the decision-making process of trained models. In this paper, we present a new approach that has been successful in gathering insights on the transcription process in Escherichia coli. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of subunits (attention heads) of the model are specialized towards identifying transcription factors and are able to successfully characterize both their binding sites and consensus sequences, uncovering both well-known and potentially novel elements involved in the initiation of the transcription process. With the specialization of the attention heads occurring automatically, we believe transformer models to be of high interest towards the creation of explainable neural networks in this field.
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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.