AIMC Topic: Phylogeny

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Genome Mining and Chemistry-Driven Discovery of a Cell Wall Lipopeptide Signature for subsp. Ancestral Lineage.

ACS infectious diseases
subsp. () causes Johne's disease (JD), a chronic infection responsible for considerable economic losses to dairy industries worldwide. Genetically clonal, has evolved into three distinct genetic lineages designated CII, for bovine strains, and SI ...

Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.

Systematic biology
Deep learning has previously shown success in automatically generating morphological traits that carry a phylogenetic signal. In this paper, we explore combining molecular data with deep learning derived morphological traits from images of pinned ins...

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...

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...

An efficient deep learning method for amino acid substitution model selection.

Journal of evolutionary biology
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.)
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
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)
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
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...