AIMC Topic: Phylogeny
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Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.
Molecular biology and evolution
Nov 1, 2024
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
Nov 1, 2024
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
Sep 1, 2024
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)
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...
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