AIMC Topic: Phylogeny

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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...
Evolution, Molecular Influenza, Human Influenza Vaccines Humans Machine Learning Phylogeny
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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...
Machine Learning Phylogeny
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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 ...
Knowledge Bases Proteins Amino Acid Sequence Software Phylogeny
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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...
Phylogeny Deep Learning Color Artificial Intelligence Temperature Animals Species Specificity Skin Pigmentation Insecta Moths Climate Altitude Biodiversity Genetic Variation
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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 Phylogeny Principal Component Analysis Animals Arachnida Polymorphism, Single Nucleotide Cluster Analysis
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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...
Polyploidy Repetitive Sequences, Nucleic Acid Nicotiana Genome, Plant DNA, Plant Unsupervised Machine Learning Triticum Phylogeny Genomics Evolution, Molecular
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StomataCounter: a neural network for automatic stomata identification and counting.

The New phytologist Jul 4, 2019
Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user-friendly, fully automated methods to perform the task of identifying and counting st...
Automation Linear Models Plant Stomata Image Processing, Computer-Assisted Databases as Topic Humans Neural Networks, Computer Phylogeny
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Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated Strain NS-MIE from Agricultural Soil.

BioMed research international May 26, 2019
Numerous technologies and approaches have been used in the past few decades to remove hexavalent chromium (Cr[VI]) in wastewater and the environment. However, these conventional technologies are not economical and efficient in removing Cr(VI) at a ve...
Chromium Phylogeny Neural Networks, Computer Analysis of Variance Biodegradation, Environmental Agriculture Soil Acinetobacter Soil Pollutants
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Phy-PMRFI: Phylogeny-Aware Prediction of Metagenomic Functions Using Random Forest Feature Importance.

IEEE transactions on nanobioscience Apr 24, 2019
High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomi...
Decision Trees Microbiota Pharynx High-Throughput Nucleotide Sequencing Metagenomics Phylogeny Support Vector Machine Databases, Genetic Humans Algorithms Metagenome
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Multi-task learning improves ancestral state reconstruction.

Theoretical population biology Jan 11, 2019
We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to e...
Likelihood Functions Computer Simulation Humans Phylogeny Learning Stochastic Processes Machine Learning Models, Biological Animals Phenotype Biological Evolution Mammals
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