AIMC Topic: Phylogeny

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Learning machine approach reveals microbial signatures of diet and sex in dog.

PloS one Aug 17, 2020
The characterization of the microbial population of many niches of the organism, as the gastrointestinal tract, is now possible thanks to the use of high-throughput DNA sequencing technique. Several studies in the companion animals field already inve...
Feces Animals Dogs Principal Component Analysis Male Discriminant Analysis Microbiota Diet Phylogeny Machine Learning Algorithms Female
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Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.

World journal of microbiology & biotechnology Jul 2, 2020
Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM c...
Thailand Phylogeny Salmonella Feces Machine Learning Algorithms Animals Humans Salmonella Infections Bacterial Typing Techniques DNA Fingerprinting Nucleic Acid Denaturation Chickens Serogroup Serotyping Polymerase Chain Reaction
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Machine learning-based analyses support the existence of species complexes for and .

Parasitology Jun 16, 2020
Human strongyloidiasis is a serious disease mostly attributable to Strongyloides stercoralis and to a lesser extent Strongyloides fuelleborni, a parasite mainly of non-human primates. The role of animals as reservoirs of human-infecting Strongyloides...
Phylogeny Feces Genotype Computational Biology Humans Machine Learning Animals Genetic Speciation RNA, Ribosomal, 18S Electron Transport Complex IV Multilocus Sequence Typing Strongyloides Genes, Helminth Dogs Disease Reservoirs Strongyloides stercoralis Primates Dog Diseases Strongyloidiasis Haplotypes
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Predicting host taxonomic information from viral genomes: A comparison of feature representations.

PLoS computational biology May 26, 2020
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...
Linear Models Genome, Viral Viruses Caudovirales Area Under Curve Nucleotides Metagenomics Algorithms Databases, Factual Phylogeny Bacteria Computational Biology Support Vector Machine Sequence Analysis, DNA
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Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

Viruses May 19, 2020
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying...
Databases, Genetic Mutation Anti-Retroviral Agents HIV-1 Phylogeny Drug Resistance, Viral Deep Learning Humans HIV Infections Neural Networks, Computer Genotype
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PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data.

IEEE journal of biomedical and health informatics May 11, 2020
Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introdu...
Neural Networks, Computer Algorithms Genetic Predisposition to Disease Phenotype Gastrointestinal Microbiome Phylogeny Databases, Genetic Deep Learning Metagenomics
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An Application of Random Walk Resampling to Phylogenetic HMM Inference and Learning.

IEEE transactions on nanobioscience May 8, 2020
Statistical resampling methods are widely used for confidence interval placement and as a data perturbation technique for statistical inference and learning. An important assumption of popular resampling methods such as the standard bootstrap is that...
Machine Learning Computer Simulation Phylogeny Computational Biology Markov Chains Sequence Alignment
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TMTCPT: The Tree Method based on the Taxonomic Categorization and the Phylogenetic Tree for fine-grained categorization.

Bio Systems Apr 28, 2020
Fine-grained categorization is one of the most challenging problems in machine vision. Recently, the presented methods have been based on convolutional neural networks, increasing the accuracy of classification very significantly. Inspired by these m...
Deep Learning Pattern Recognition, Automated Classification Neural Networks, Computer Animals Machine Learning Birds Phylogeny
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Birds have peramorphic skulls, too: anatomical network analyses reveal oppositional heterochronies in avian skull evolution.

Communications biology Apr 24, 2020
In contrast to the vast majority of reptiles, the skulls of adult crown birds are characterized by a high degree of integration due to bone fusion, e.g., an ontogenetic event generating a net reduction in the number of bones. To understand this proce...
Age Factors Skull Biological Evolution Animals Dinosaurs Birds Neural Networks, Computer Phylogeny
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gammaBOriS: Identification and Taxonomic Classification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning.

Scientific reports Apr 21, 2020
The biology of bacterial cells is, in general, based on information encoded on circular chromosomes. Regulation of chromosome replication is an essential process that mostly takes place at the origin of replication (oriC), a locus unique per chromoso...
Replication Origin Machine Learning Base Sequence Software Nucleotide Motifs Phylogeny Consensus Sequence Models, Genetic Gammaproteobacteria
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