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Phylogeny

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Do protein language models learn phylogeny?

Briefings in bioinformatics
Deep machine learning demonstrates a capacity to uncover evolutionary relationships directly from protein sequences, in effect internalising notions inherent to classical phylogenetic tree inference. We connect these two paradigms by assessing the ca...

Learning genotype-phenotype associations from gaps in multi-species sequence alignments.

Briefings in bioinformatics
Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs a neural network...

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.

Molecular biology and evolution
Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant cha...

Tree Sequences as a General-Purpose Tool for Population Genetic Inference.

Molecular biology and evolution
As population genetic data increase in size, new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient but are not interchangeable with exis...

Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning.

Environmental microbiology
Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 16S rRNA gene surveys typically identify tens to hundreds of disease-associate bacteria as putative p...

A machine-learning-based alternative to phylogenetic bootstrap.

Bioinformatics (Oxford, England)
MOTIVATION: Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic ana...

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Systematic biology
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computation...

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Bioinformatics (Oxford, England)
MOTIVATION: Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the compl...

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.

Briefings in bioinformatics
Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey...