ArticleCommunications biology2025
Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.PLoS computational biology · 2026Article
- Evaluating ensemble learning approaches for horizontal gene transfer detection.Scientific reports · 2026Article
- Stochastic intracellular calcium dynamics show preserved structures identified by deep learning classification.PLoS computational biology · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.bioRxiv : the preprint server for biology · 2026Article
- Lineage-specific regulatory evolution: insights from massively parallel reporter assays.Current opinion in genetics & development · 2025Review
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2 authors.
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Abstract
Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. Prevailing detection methods treat the design of the classifier as a discrete phase, assuming that high classification accuracy is the sole prerequisite for precise detection. This frequently steers method development toward classification-driven optimizations that can inadvertently impede detection. We present FASTER-NN, a CNN classifier designed specifically for the precise detection of natural selection. It has higher sensitivity than state-of-the-art CNN classifiers while only processing allele frequencies and genomic positions through dilated convolutions to maximize data reuse. As a result, execution time is invariant to the sample size and the chromosome length, creating a highly suitable solution for large-scale, whole-genome scans. Furthermore, FASTER-NN can accurately identify selective sweeps in recombination hotspots, which is a highly challenging detection problem with very limited theoretical treatment to date.
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