AIMC Topic: Evolution, Molecular

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Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model.

PloS one Dec 31, 2024
Protein glycosylation, a vital post-translational modification, is pivotal in various biological processes and disease pathogenesis. Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable...
Databases, Protein Algorithms Evolution, Molecular Protein Processing, Post-Translational Humans Proteins Glycoproteins Acetylglucosamine Computational Biology Machine Learning Glycosylation
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FluPMT: Prediction of Predominant Strains of Influenza A Viruses via Multi-Task Learning.

IEEE/ACM transactions on computational biology and bioinformatics Oct 9, 2024
Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vacci...
Machine Learning Algorithms Influenza A virus Humans Influenza, Human Influenza Vaccines Hemagglutinin Glycoproteins, Influenza Virus Computational Biology Evolution, Molecular
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CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.

Interdisciplinary sciences, computational life sciences Sep 30, 2024
The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as t...
Genome, Bacterial Bacteria Computational Biology Evolution, Molecular Multigene Family Machine Learning Biological Products Anti-Bacterial Agents
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments.

Molecular phylogenetics and evolution Aug 30, 2024
Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the phylogenetic tree reconstruction based on the Maximum Likelihood method. In this study, we present ...
Evolution, Molecular Likelihood Functions Sequence Alignment Phylogeny Models, Genetic Neural Networks, Computer Machine Learning
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Deep learning can predict subgenome dominance in ancient but not in neo/synthetic polyploidized genomes.

The Plant journal : for cell and molecular biology Aug 12, 2024
Deep learning offers new approaches to investigate the mechanisms underlying complex biological phenomena, such as subgenome dominance. Subgenome dominance refers to the dominant expression and/or biased fractionation of genes in one subgenome of all...
Polyploidy Genome, Plant Gene Expression Regulation, Plant Neural Networks, Computer Deep Learning Evolution, Molecular Promoter Regions, Genetic DNA Methylation
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Reliable estimation of tree branch lengths using deep neural networks.

PLoS computational biology Aug 5, 2024
A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the t...
Neural Networks, Computer Machine Learning Deep Learning Computational Biology Models, Genetic Algorithms Phylogeny Sequence Alignment Evolution, Molecular Likelihood Functions
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Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.

PloS one May 14, 2024
To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combin...
Machine Learning Algorithms Polymorphism, Single Nucleotide Proteins Humans Computational Biology Evolution, Molecular Mutation
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Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning.

Journal of chemical information and modeling Apr 30, 2024
L2 β-lactamases, serine-based class A β-lactamases expressed by , play a pivotal role in antimicrobial resistance (AMR). However, limited studies have been conducted on these important enzymes. To understand the coevolutionary dynamics of L2 β-lactam...
Deep Learning Evolution, Molecular beta-Lactamases Protein Conformation Molecular Dynamics Simulation Stenotrophomonas maltophilia
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Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

Scientific reports Apr 22, 2024
The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understandin...
Computational Biology Machine Learning Genes, Essential Evolution, Molecular Neoplasms Humans Protein Interaction Maps
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Self-replicating artificial neural networks give rise to universal evolutionary dynamics.

PLoS computational biology Mar 28, 2024
In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRAN...
Mutation Mutation Rate Biological Evolution Genotype Neural Networks, Computer Evolution, Molecular
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