ArticleMolecular biology and evolution2021
Evolutionary Sparse Learning for Phylogenomics.
Article in Molecular biology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Review
- Efficient algorithms for simulating sequences along a phylogenetic tree.Bioinformatics (Oxford, England) · 2026Article
- MyESL: A Software for Evolutionary Sparse Learning in Molecular Phylogenetics and Genomics.Molecular biology and evolution · 2025Article
- From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions.Genome biology and evolution · 2025Review
- Article
- Evolutionary sparse learning reveals the shared genetic basis of convergent traits.Nature communications · 2025Article
- Assessment of phylogenetic informativeness in mitochondrial and nuclear genes for mammalian systematics using sparse learning.Frontiers in bioinformatics · 2025Article
- MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing.Molecular biology and evolution · 2024Article
- Discovering Fragile Clades and Causal Sequences in Phylogenomics by Evolutionary Sparse Learning.Molecular biology and evolution · 2024Article
- A machine-learning-based alternative to phylogenetic bootstrap.Bioinformatics (Oxford, England) · 2024Article
- Constructing phylogenetic networks via cherry picking and machine learning.Algorithms for molecular biology : AMB · 2023Article
- A LASSO-based approach to sample sites for phylogenetic tree search.Bioinformatics (Oxford, England) · 2022Article
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- Embracing Green Computing in Molecular Phylogenetics.Molecular biology and evolution · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci-such as genes, proteins, genomic segments, and positions-as parameters. Using the Least Absolute Shrinkage and Selection Operator, ESL selects only the most important genomic loci to explain a given phylogenetic hypothesis or presence/absence of a trait. ESL models do not directly involve conventional parameters such as rates of substitutions between nucleotides, rate variation among positions, and phylogeny branch lengths. Instead, ESL directly employs the concordance of variation across sequences in an alignment with the evolutionary hypothesis of interest. ESL provides a natural way to combine different molecular and nonmolecular data types and incorporate biological and functional annotations of genomic loci in model building. We propose positional, gene, function, and hypothesis sparsity scores, illustrate their use through an example, and suggest several applications of ESL. The ESL framework has the potential to drive the development of a new class of computational methods that will complement traditional approaches in evolutionary genomics, particularly for identifying influential loci and sequences given a phylogeny and building models to test hypotheses. ESL's fast computational times and small memory footprint will also help democratize big data analytics and improve scientific rigor in phylogenomics.
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