Harnessing machine learning to guide phylogenetic-tree search algorithms

Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems involving more than a handful of sequences, inferen...

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Vydáno v:	Nature communications Ročník 12; číslo 1; s. 1983 - 9
Hlavní autoři:	Azouri, Dana, Abadi, Shiran, Mansour, Yishay, Mayrose, Itay, Pupko, Tal
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 31.03.2021 Nature Publishing Group Nature Portfolio
Témata:	631/114/1305 631/114/2397 631/181/735 631/181/757 Accuracy Algorithms Animals Artificial intelligence Computer applications Computing time Databases, Genetic - statistics & numerical data Databases, Protein - statistics & numerical data Empirical analysis Evolution, Molecular Humanities and Social Sciences Humans Inference Learning algorithms Machine Learning Models, Genetic multidisciplinary Optimization Phylogenetics Phylogeny Reconstruction Run time (computers) Science Science (multidisciplinary) Search algorithms Sequences Trees
ISSN:	2041-1723, 2041-1723
On-line přístup:	Získat plný text
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