.png){kind=icon}
返回
Evolutionary Sparse Learning for Phylogenomics
.png){kind=icon}
OA
AI
DOI：10.1093/molbev/msab227.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
machine learning
phylogenetics
total evidence
phylogenomics
functional genomics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.3
论文数:
8.3K
被引数:
6.6W
机构
引用论文
.png){kind=icon}
Contentious relationships in phylogenomic studies can be driven by a handful of genes系统发育研究中的有争议的关系可以由少数基因驱动OAAI
OAAI分享收藏分享收藏
查看更多内容