AIMC Topic: Phylogeny
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A machine-learning-based alternative to phylogenetic bootstrap.
Bioinformatics (Oxford, England)
Jun 28, 2024
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
May 27, 2024
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)
Mar 29, 2024
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
Mar 27, 2024
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...
DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.
Briefings in bioinformatics
Mar 27, 2024
Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host...
Simulations of Sequence Evolution: How (Un)realistic They Are and Why.
Molecular biology and evolution
Jan 3, 2024
MOTIVATION: Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches...
Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to c...
Deep Learning from Phylogenies for Diversification Analyses.
Systematic biology
Dec 30, 2023
Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood ...
Fusang: a framework for phylogenetic tree inference via deep learning.
Nucleic acids research
Nov 10, 2023
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods ...
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