AIMC Topic: Phylogeny

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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...

DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.

Briefings in bioinformatics
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
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.)
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
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
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 ...

Inferring Historical Introgression with Deep Learning.

Systematic biology
Resolving phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary rel...

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Genome biology and evolution
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implem...

DEPP: Deep Learning Enables Extending Species Trees using Single Genes.

Systematic biology
Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phy...

Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.

Briefings in bioinformatics
LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classif...