AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Phylogeny

Showing 1 to 10 of 204 articles

Clear Filters

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...

Do protein language models learn phylogeny?

Briefings in bioinformatics
Deep machine learning demonstrates a capacity to uncover evolutionary relationships directly from protein sequences, in effect internalising notions inherent to classical phylogenetic tree inference. We connect these two paradigms by assessing the ca...

Learning genotype-phenotype associations from gaps in multi-species sequence alignments.

Briefings in bioinformatics
Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs a neural network...

Using core genome and machine learning for serovar prediction in Salmonella enterica subspecies I strains.

FEMS microbiology letters
This study presents a dual investigation of Salmonella enterica subspecies I, focusing on serovar prediction and core genome characteristics. We utilized two large genomic datasets (panX and NCBI Pathogen Detection) to test machine learning methods f...

A Machine Learning Pipeline to Screen Large In Vivo Molecular Data to Curate Disease Signatures of High Translational Potential.

Methods in molecular biology (Clifton, N.J.)
A significantly low success rate of human clinical studies has long been attributed to a capability gap, namely, an ineffective translation of the animal data to the human context. To bridge this capability gap, several correcting measures have been ...

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

Genome biology
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to in...

Equitable machine learning counteracts ancestral bias in precision medicine.

Nature communications
Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. Therapeutics and outcomes remain hidden because we lack insights that could be gained from analyzing...

Machine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data.

Cladistics : the international journal of the Willi Hennig Society
Proteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vertebrate hosts in freshwater and terrestrial environments. Despite the ubiquity of the group, key macroevolutionary processes that have driven the group...

Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.

Nucleic acids research
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...

Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.

Molecular biology and evolution
Phylogenetic inference aims at reconstructing the tree describing the evolution of a set of sequences descending from a common ancestor. The high computational cost of state-of-the-art maximum likelihood and Bayesian inference methods limits their us...