AIMC Topic: Phylogeny

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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....

Taxonomy, biological characterization and fungicide sensitivity assays of Hypomyces cornea sp. nov. causing cobweb disease on Auricularia cornea.

Fungal biology
Auricularia cornea is an important edible mushroom crop in China but the occurrence of cobweb disease has cause significance economic loss in its production. The rate of disease occurrence is 16.65% all over the country. In the present study, a new p...

Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology.

Journal of proteome research
The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field ...