ArticleGenome research2022
Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.
Article in Genome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- The persistence and loss of hard selective sweeps amid admixture in ancient Eurasians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Augmented prediction of multi-species protein-RNA interactions using evolutionary conservation of RNA-binding proteins.Nature communications · 2026Article
- Cross-species prediction reveals chromatin regions with increased accessibility in humans.Science advances · 2026Article
- Deep learning application for genomic data analysis.BMB reports · 2026Review
- Motif-based models accurately predict cell type-specific distal regulatory elements.Nature communications · 2025Article
- Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge.Nucleic acids research · 2025Article
- Iterative improvement of deep learning models using synthetic regulatory genomics.Genome research · 2025Article
- The persistence and loss of hard selective sweeps amid ancient human admixture.bioRxiv : the preprint server for biology · 2025Article
- Ultra-fast variant effect prediction using biophysical transcription factor binding models.Nucleic acids research · 2025Article
- "Frustratingly easy" domain adaptation for cross-species transcription factor binding prediction.bioRxiv : the preprint server for biology · 2025Article
- deepTFBS: Improving within- and Cross-Species Prediction of Transcription Factor Binding Using Deep Multi-Task and Transfer Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge.bioRxiv : the preprint server for biology · 2025Article
- Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals.Nature communications · 2024Article
- Emergence of enhancers at late DNA replicating regions.Nature communications · 2024Article
- Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.PLoS genetics · 2023Article
- Network models to enhance the translational impact of cross-species studies.Nature reviews. Neuroscience · 2023Review
- Decoding enhancer complexity with machine learning and high-throughput discovery.Genome biology · 2023Review
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals.Frontiers in big data · 2023Article
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7 authors.
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Abstract
The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence models of cell type-specific genomic occupancy of a TF in one species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross-species predictive performance is consistently worse than within-species performance, which we show is caused in part by species-specific repeats. To account for this domain shift, we use an augmented network architecture to automatically discourage learning of training species-specific sequence features. This domain adaptation approach corrects for prediction errors on species-specific repeats and improves overall cross-species model performance. Our results show that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats.
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