AIMC Topic: Insect Vectors

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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...
Machine Learning Animals Ecosystem Cameroon Polymorphism, Single Nucleotide Models, Genetic Selection, Genetic Adaptation, Physiological Insect Vectors Genome, Insect Recombination, Genetic Anopheles Chromosome Inversion
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Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas.

PloS one May 11, 2015
BACKGROUND: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when t...
Humans Algorithms Female Reproducibility of Results Animals Male Thailand Incidence Models, Theoretical Support Vector Machine Dengue Virus Morbidity Climate Aedes Environment Insect Vectors Severe Dengue
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Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors.

Journal of medical entomology Sep 3, 2019
Vector-borne Chagas disease is endemic to the Americas and imposes significant economic and social burdens on public health. In a previous contribution, we presented an automated identification system that was able to discriminate among 12 Mexican an...
Animals Brazil Deep Learning Mexico Classification Chagas Disease Insect Vectors Triatominae
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