AIMC Topic: Phylogeny
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Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.
Molecular biology and evolution
Apr 1, 2025
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...
Using core genome and machine learning for serovar prediction in Salmonella enterica subspecies I strains.
FEMS microbiology letters
Jan 10, 2025
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...
An efficient deep learning method for amino acid substitution model selection.
Journal of evolutionary biology
Jan 3, 2025
Amino acid substitution models play an important role in studying the evolutionary relationships among species from protein sequences. The amino acid substitution model consists of a large number of parameters; therefore, it is estimated from hundred...
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.)
Jan 1, 2025
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 ...
Unveiling the ghost: machine learning's impact on the landscape of virology.
The Journal of general virology
Jan 1, 2025
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for address...
BetaAlign: a deep learning approach for multiple sequence alignment.
Bioinformatics (Oxford, England)
Dec 26, 2024
MOTIVATION: Multiple sequence alignments (MSAs) are extensively used in biology, from phylogenetic reconstruction to structure and function prediction. Here, we suggest an out-of-the-box approach for the inference of MSAs, which relies on algorithms ...
Deep learning insights into distinct patterns of polygenic adaptation across human populations.
Nucleic acids research
Dec 11, 2024
Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter t...
Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.
Nucleic acids research
Nov 27, 2024
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...
Do protein language models learn phylogeny?
Briefings in bioinformatics
Nov 22, 2024
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
Nov 22, 2024
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...
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