AIMC Topic: Phylogeny

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Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks.

Nature communications Jul 6, 2022
Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, gene...
Humans Computer Simulation Phylogeny Male Deep Learning Disease Outbreaks Bayes Theorem
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Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.

BMC genomics May 7, 2022
BACKGROUND: We combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5,...
Genetics, Population Haplotypes Phylogeography Europe Evolution, Molecular Humans Bayes Theorem Phylogeny Unsupervised Machine Learning DNA, Mitochondrial
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A new linear combination method of haplogroup distribution central vectors to model population admixtures.

Molecular genetics and genomics : MGG Apr 11, 2022
We introduce a novel population genetic approach suitable to model the origin and relationships of populations, using new computation methods analyzing Hg frequency distributions. Hgs were selected into groups which show correlated frequencies in sub...
Phylogeny DNA, Mitochondrial Mercury Artificial Intelligence Algorithms Genetics, Population Haplotypes Hungary
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Current progress and open challenges for applying deep learning across the biosciences.

Nature communications Apr 1, 2022
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future pe...
Phylogeny Deep Learning Proteins Computational Biology Systems Biology
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EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Journal of molecular biology Mar 11, 2022
Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of ev...
Phylogeny Proteins Sequence Alignment Internet Use Protein Conformation Machine Learning Software Algorithms Humans
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Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.

Scientific reports Feb 14, 2022
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...
Humans Prognosis Genome, Bacterial Phylogeny Antitubercular Agents Phenotype Tuberculosis, Multidrug-Resistant Mycobacterium tuberculosis Data Accuracy Whole Genome Sequencing Drug Resistance, Multiple, Bacterial Deep Learning Cohort Studies Microbial Sensitivity Tests Mutation
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NIFtHool: an informatics program for identification of NifH proteins using deep neural networks.

F1000Research Feb 9, 2022
Atmospheric nitrogen fixation carried out by microorganisms has environmental and industrial importance, related to the increase of soil fertility and productivity. The present work proposes the development of a new high precision system that allows ...
Oxidoreductases Neural Networks, Computer Bacterial Proteins Bacteria Informatics Phylogeny
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Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards.

PloS one Jan 21, 2022
Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause prob...
Phenotype Animals Machine Learning Phylogeny Multivariate Analysis Lizards
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Re-evaluating Deep Neural Networks for Phylogeny Estimation: The Issue of Taxon Sampling.

Journal of computational biology : a journal of computational molecular cell biology Jan 5, 2022
Deep neural networks (DNNs) have been recently proposed for quartet tree phylogeny estimation. Here, we present a study evaluating recently trained DNNs in comparison to a collection of standard phylogeny estimation methods on a heterogeneous collect...
Classification Databases, Genetic Evolution, Molecular Amino Acid Sequence Deep Learning Computational Biology Neural Networks, Computer Computer Simulation Phylogeny
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Classification and Identification of Archaea Using Single-Cell Raman Ejection and Artificial Intelligence: Implications for Investigating Uncultivated Microorganisms.

Analytical chemistry Dec 15, 2021
Archaea can produce special cellular components such as polyhydroxyalkanoates, carotenoids, rhodopsin, and ether lipids, which have valuable applications in medicine and green energy production. Most of the archaeal species are uncultivated, posing c...
RNA, Ribosomal, 16S Lipids Artificial Intelligence In Situ Hybridization, Fluorescence Phylogeny
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