AIMC Topic: Phylogeny

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Scalable classification of organisms into a taxonomy using hierarchical supervised learners.

Journal of bioinformatics and computational biology Oct 29, 2020
Accurately identifying organisms based on their partially available genetic material is an important task to explore the phylogenetic diversity in an environment. Specific fragments in the DNA sequence of a living organism have been defined as DNA ba...
Fungi Phylogeny Algorithms DNA Barcoding, Taxonomic Deep Learning Rodentia Animals Classification Support Vector Machine Ferns Chiroptera Birds Databases, Genetic
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Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Proceedings of the National Academy of Sciences of the United States of America Oct 23, 2020
Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution ...
South America Phylogeography Africa, Western Pollen Neural Networks, Computer Phylogeny Fossils Machine Learning Africa Microscopy
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lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA.

Scientific data Oct 5, 2020
Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,19...
Knowledge Bases Organ Specificity Molecular Sequence Annotation Quantitative Trait Loci Humans Phylogeny RNA, Long Noncoding
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Pollen analysis using multispectral imaging flow cytometry and deep learning.

The New phytologist Sep 22, 2020
Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensi...
Deep Learning Flow Cytometry Pollination Pollen 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 Dogs Animals Machine Learning Male Microbiota Principal Component Analysis Algorithms Diet Discriminant Analysis Female Phylogeny
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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...
Serogroup Serotyping Polymerase Chain Reaction Salmonella Infections Salmonella Feces Chickens Bacterial Typing Techniques DNA Fingerprinting Nucleic Acid Denaturation Humans Machine Learning Algorithms Animals Thailand Phylogeny
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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...
Strongyloidiasis Electron Transport Complex IV Genes, Helminth Strongyloides Strongyloides stercoralis Feces Disease Reservoirs Genotype Primates Computational Biology Humans Genetic Speciation Dogs Machine Learning Dog Diseases Animals Haplotypes Phylogeny RNA, Ribosomal, 18S Multilocus Sequence Typing
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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...
Computational Biology Support Vector Machine Sequence Analysis, DNA Area Under Curve Nucleotides Metagenomics Linear Models Algorithms Databases, Factual Phylogeny Bacteria Genome, Viral Viruses Caudovirales
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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...
Genotype Databases, Genetic Mutation Humans Phylogeny Neural Networks, Computer HIV-1 Anti-Retroviral Agents Deep Learning Drug Resistance, Viral HIV Infections
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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...
Gastrointestinal Microbiome Databases, Genetic Metagenomics Phenotype Phylogeny Deep Learning Genetic Predisposition to Disease Neural Networks, Computer Algorithms
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