AIMC Topic: Selection, Genetic

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Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Nature communications Oct 17, 2018
Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. He...
Mycobacterium tuberculosis Gene Frequency Selection, Genetic Genome, Bacterial Machine Learning Drug Resistance, Bacterial
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A deep convolutional neural network approach for predicting phenotypes from genotypes.

Planta Aug 12, 2018
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted b...
Genetic Association Studies Models, Genetic Plants Neural Networks, Computer Machine Learning Genome-Wide Association Study Selection, Genetic
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diploS/HIC: An Updated Approach to Classifying Selective Sweeps.

G3 (Bethesda, Md.) May 31, 2018
Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective s...
Base Sequence Selection, Genetic Chromosomes Genome, Insect Recombination, Genetic Neural Networks, Computer ROC Curve Anopheles Machine Learning Image Processing, Computer-Assisted Animals Genotype
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Supervised Machine Learning for Population Genetics: A New Paradigm.

Trends in genetics : TIG Jan 10, 2018
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly be...
Humans Supervised Machine Learning Selection, Genetic Biological Evolution Genetics, Population Data Mining Datasets as Topic Genome, Human
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Mutant selection window of four quinolone antibiotics against clinical isolates of Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis.

Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy Dec 29, 2017
Community-acquired pneumonia and otitis media are caused by several bacterial species, including Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis. For the treatment of these diseases, various quinolones are frequently used....
Otitis Media Selection, Genetic Streptococcus pneumoniae Microbial Sensitivity Tests DNA Topoisomerase IV Pneumonia, Bacterial Humans Quinolones DNA Gyrase Haemophilus influenzae DNA Mutational Analysis Anti-Bacterial Agents Moraxella catarrhalis Drug Resistance, Bacterial
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Cotton genotypes selection through artificial neural networks.

Genetics and molecular research : GMR Sep 27, 2017
Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explore...
Selective Breeding Gossypium Quantitative Trait, Heritable Plant Breeding Neural Networks, Computer Genotype Models, Genetic Selection, Genetic Cotton Fiber
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Selection plays the hand it was dealt: evidence that human adaptation commonly targets standing genetic variation.

Genome biology Aug 1, 2017
Using a powerful machine learning approach, a recent study of human genomes has revealed widespread footprints of recent positive selection on standing genetic variation.
Genetics, Population Linkage Disequilibrium Biological Evolution Adaptation, Biological Genetic Variation Genome, Human Selection, Genetic Humans Machine Learning Models, Genetic Sequence Analysis, DNA
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How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation.

PLoS computational biology Apr 6, 2017
One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can nat...
Humans Environment Biological Evolution Computational Biology Machine Learning Models, Biological Phenotype Selection, Genetic Learning
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Artificial intelligence in the selection of common bean genotypes with high phenotypic stability.

Genetics and molecular research : GMR Apr 28, 2016
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean g...
Phenotype Genotype Models, Genetic Neural Networks, Computer Plant Breeding Edible Grain Selection, Genetic Phaseolus
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Identifying targets of selection in mosaic genomes with machine learning: applications in Anopheles gambiae for detecting sites within locally adapted chromosomal inversions.

Molecular ecology Apr 20, 2016
Chromosomal inversions are important structural changes that may facilitate divergent selection when they capture co-adaptive loci in the face of gene flow. However, identifying selection targets within inversions can be challenging. The high degrees...
Insect Vectors Genome, Insect Recombination, Genetic Anopheles Chromosome Inversion Machine Learning Models, Genetic Animals Selection, Genetic Adaptation, Physiological Ecosystem Cameroon Polymorphism, Single Nucleotide
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