AIMC Topic: Phylogeny

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Employing phylogenetic tree shape statistics to resolve the underlying host population structure.

BMC bioinformatics Nov 10, 2021
BACKGROUND: Host population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a populat...
Algorithms Phylogeny ROC Curve Machine Learning Support Vector Machine
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Co-evolution based machine-learning for predicting functional interactions between human genes.

Nature communications Nov 9, 2021
Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary question...
Phylogeny Sequence Analysis, DNA Evolution, Molecular DNA Repair Humans Machine Learning
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Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system.

Molecular ecology resources Oct 31, 2021
Delimiting species boundaries is a major goal in evolutionary biology. An increasing volume of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and de...
Gene Flow Cactaceae Phylogeny Species Specificity Biological Evolution Deep Learning Bayes Theorem
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Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils.

Molecules (Basel, Switzerland) Oct 17, 2021
Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, th...
Machine Learning Microsporum Structure-Activity Relationship Arthrodermataceae Data Collection Complex Mixtures Anti-Infective Agents Phylogeny Oils, Volatile
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Microbiome Preprocessing Machine Learning Pipeline.

Frontiers in immunology Jun 18, 2021
BACKGROUND: 16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ...
Colitis Pregnancy Bacteria Animals Mucositis RNA, Ribosomal, 16S Progesterone Female Phylogeny Humans Gastrointestinal Microbiome Hematopoietic Stem Cell Transplantation Adult Mice Neural Networks, Computer Interleukin-1alpha Machine Learning
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Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen.

Nature communications Jun 10, 2021
Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere...
Eukaryota Genome INDEL Mutation Ascomycota Pedigree Meiosis Crossing Over, Genetic Genes, Duplicate Sequence Inversion Chromosomes Arabidopsis Retroelements Genomics Polymorphism, Single Nucleotide Models, Genetic Evolution, Molecular Genetic Variation Polymorphism, Genetic Computer Simulation Phylogeny Genome-Wide Association Study Machine Learning
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Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

PloS one Jun 1, 2021
Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological netwo...
Machine Learning Pest Control, Biological Diptera Host-Parasite Interactions Animals Lepidoptera Food Chain Hymenoptera Phylogeny Introduced Species Forests
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SPASOS 1.1: a program for the inference of ancestral shape ontogenies.

Cladistics : the international journal of the Willi Hennig Society May 28, 2021
We recently published a method to infer ancestral landmark-based shape ontogenies that takes into account the possible existence of changes in developmental timing. Here we describe SPASOS, a software to perform that analysis. SPASOS is an open-sourc...
Species Specificity Biological Evolution Phylogeny Biological Ontologies Software Algorithms
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Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

PLoS pathogens Apr 20, 2021
The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for eme...
Spike Glycoprotein, Coronavirus Genome, Viral Machine Learning Biological Evolution Animals Coronaviridae Phylogeny Coronaviridae Infections
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An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods.

Scientific reports Apr 19, 2021
Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtain...
Machine Learning Gastropoda Antarctic Regions Genetic Speciation Gene Expression Profiling Animals Sequence Analysis, DNA Ecosystem Gene Expression Regulation Phylogeny
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