AIMC Topic: Phylogeography
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Machine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data.
Cladistics : the international journal of the Willi Hennig Society
Mar 6, 2025
Proteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vertebrate hosts in freshwater and terrestrial environments. Despite the ubiquity of the group, key macroevolutionary processes that have driven the group...
Phylogenomics and phylogeographic model testing using convolutional neural networks reveal a history of recent admixture in the Canarian Kleinia neriifolia.
Molecular ecology
Oct 19, 2024
Multiple-island endemics (MIE) are considered ideal natural subjects to study patterns of island colonization that involve recent population-level genetic processes. Kleinia neriifolia is a Canarian MIE widespread across the archipelago, which exhibi...
Artificial intelligence enables unified analysis of historical and landscape influences on genetic diversity.
Molecular phylogenetics and evolution
Jun 12, 2024
While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversit...
Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.
BMC genomics
May 7, 2022
BACKGROUND: We combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5,...
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 ...
WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants.
Genome biology
May 28, 2019
The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks lim...
Machine Learning Biogeographic Processes from Biotic Patterns: A New Trait-Dependent Dispersal and Diversification Model with Model Choice By Simulation-Trained Discriminant Analysis.
Systematic biology
Dec 28, 2015
Current statistical biogeographical analysis methods are limited in the ways ecology can be related to the processes of diversification and geographical range evolution, requiring conflation of geography and ecology, and/or assuming ecologies that ar...
Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.
PloS one
Nov 24, 2015
Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biolog...
Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.
Systematic biology
May 27, 2024
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computation...
Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2019
Phylogeography research involving virus spread and tree reconstruction relies on accurate geographic locations of infected hosts. Insufficient level of geographic information in nucleotide sequence repositories such as GenBank motivates the use of na...
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