AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Phylogeny

Showing 51 to 60 of 204 articles

Clear Filters

Supervised learning and model analysis with compositional data.

PLoS computational biology
Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in microbiome research. However, due to the compositionality and sparsity, existing techniques are ofte...

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Genome biology and evolution
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implem...

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.

Viruses
Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural ...

A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.

NPJ systems biology and applications
Shape measurements are crucial for evolutionary and developmental biology; however, they present difficulties in the objective and automatic quantification of arbitrary shapes. Conventional approaches are based on anatomically prominent landmarks, wh...

ModelRevelator: Fast phylogenetic model estimation via deep learning.

Molecular phylogenetics and evolution
Selecting the best model of sequence evolution for a multiple-sequence-alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, ...

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

The New phytologist
Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling...

Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.

Molecular phylogenetics and evolution
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely ...

Fusang: a framework for phylogenetic tree inference via deep learning.

Nucleic acids research
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods ...

Inferring Historical Introgression with Deep Learning.

Systematic biology
Resolving phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary rel...

Deep Learning from Phylogenies for Diversification Analyses.

Systematic biology
Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood ...