AIMC Topic: Phylogeny

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Harnessing artificial intelligence-driven approach for enhanced indole-3-acetic acid from the newly isolated Streptomyces rutgersensis AW08.

Environmental research
Indole-3-acetic acid (IAA) derived from Actinobacteria fermentations on agro-wastes constitutes a safer and low-cost alternative to synthetic IAA. This study aims to select a high IAA-producing Streptomyces-like strain isolated from Lake Oubeira sedi...

Phylogenetic and Molecular Characteristics of Wild Bird-Origin Avian Influenza Viruses Circulating in Poland in 2018-2022: Reassortment, Multiple Introductions, and Wild Bird-Poultry Epidemiological Links.

Transboundary and emerging diseases
Since 2020, a significant increase in the severity of H5N highly pathogenic avian influenza (HPAI) epidemics in poultry and wild birds has been observed in Poland. To further investigate the genetic diversity of HPAI H5N viruses of clade 2.3.4.4b, HP...

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

PLoS computational biology
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in opti...

Genomic language model predicts protein co-regulation and function.

Nature communications
Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function par...

Learning From an Artificial Neural Network in Phylogenetics.

IEEE/ACM transactions on computational biology and bioinformatics
We show that an iterative ansatz of deep learning and human intelligence guided simplification may lead to surprisingly simple solutions for a difficult problem in phylogenetics. Distinguishing Farris and Felsenstein trees is a longstanding problem i...

Applications of machine learning in phylogenetics.

Molecular phylogenetics and evolution
Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select s...

Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.

BMC bioinformatics
The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computati...

Identification of B cell subsets based on antigen receptor sequences using deep learning.

Frontiers in immunology
B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispe...

Multi-armed bandits, Thomson sampling and unsupervised machine learning in phylogenetic graph search.

Cladistics : the international journal of the Willi Hennig Society
A phylogenetic graph search relies on a large number of highly parameterized search procedures (e.g. branch-swapping, perturbation, simulated annealing, genetic algorithm). These procedures vary in effectiveness over datasets and at alternative point...

Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning.

Scientific reports
The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C....