AIMC Topic: Phylogeny

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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...
Markov Chains Sequence Alignment Machine Learning Phylogeny Computer Simulation Computational Biology
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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...
Birds Phylogeny Pattern Recognition, Automated Classification Deep Learning Machine Learning Animals Neural Networks, Computer
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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...
Neural Networks, Computer Animals Birds Skull Biological Evolution Phylogeny Dinosaurs Age Factors
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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...
Software Phylogeny Models, Genetic Machine Learning Replication Origin Consensus Sequence Gammaproteobacteria Base Sequence Nucleotide Motifs
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Predicting the short-term success of human influenza virus variants with machine learning.

Proceedings. Biological sciences Apr 8, 2020
Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system...
Phylogeny Evolution, Molecular Influenza, Human Humans Machine Learning Influenza Vaccines
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Incorporating biological structure into machine learning models in biomedicine.

Current opinion in biotechnology Jan 18, 2020
In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexp...
Phylogeny Machine Learning
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UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase).

Journal of proteomics Dec 14, 2019
UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and ...
Phylogeny Proteins Knowledge Bases Amino Acid Sequence Software
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Artificial intelligence reveals environmental constraints on colour diversity in insects.

Nature communications Oct 7, 2019
Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gr...
Color Biodiversity Insecta Climate Genetic Variation Phylogeny Deep Learning Temperature Animals Skin Pigmentation Artificial Intelligence Altitude Moths Species Specificity
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A demonstration of unsupervised machine learning in species delimitation.

Molecular phylogenetics and evolution Jul 16, 2019
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now usin...
Unsupervised Machine Learning Principal Component Analysis Polymorphism, Single Nucleotide Animals Arachnida Cluster Analysis Phylogeny
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PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.

BMC genomics Jul 12, 2019
BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In...
Triticum Nicotiana Evolution, Molecular Phylogeny Repetitive Sequences, Nucleic Acid Genomics Polyploidy Unsupervised Machine Learning Genome, Plant DNA, Plant
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