AI Medical Compendium Topic

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

Phylogeny

Showing 181 to 190 of 204 articles

Clear Filters

A novel reassortant avian influenza H4N6 virus isolated from an environmental sample during a surveillance in Maharashtra, India.

The Indian journal of medical research
BACKGROUND & OBJECTIVES: Low pathogenic avian influenza (LPAI) viruses cause mild clinical illness in domestic birds. Migratory birds are a known reservoir for all subtypes of avian influenza (AI) viruses. The objective of the study was to characteri...

A novel deep learning method for predictive modeling of microbiome data.

Briefings in bioinformatics
With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as d...

Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Escherichia coli is a species of bacteria that can be present in a wide variety of mammalian hosts and potentially soil environments. E. coli has an open genome and can show considerable diversity in gene content between isolates. It is a reasonable ...

Supervised learning on phylogenetically distributed data.

Bioinformatics (Oxford, England)
MOTIVATION: The ability to develop robust machine-learning (ML) models is considered imperative to the adoption of ML techniques in biology and medicine fields. This challenge is particularly acute when data available for training is not independent ...

Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks.

Molecular biology and evolution
Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that...

ModelTeller: Model Selection for Optimal Phylogenetic Reconstruction Using Machine Learning.

Molecular biology and evolution
Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. Although model selection is regarded as a fundamental step in phylogenetics, existing methods for this...

Searching for Models for Psychological Science: A Possible Contribution of Simulation.

Integrative psychological & behavioral science
The problem of the theoretical precariousness of psychology requires defining, at an epistemological level, its concepts and languages and the use of models for finding core concepts and building more or less 'hard' theories. After reviewing some mai...

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the...

Deep Residual Neural Networks Resolve Quartet Molecular Phylogenies.

Molecular biology and evolution
Phylogenetic inference is of fundamental importance to evolutionary as well as other fields of biology, and molecular sequences have emerged as the primary data for this task. Although many phylogenetic methods have been developed to explicitly take ...

Accurate Inference of Tree Topologies from Multiple Sequence Alignments Using Deep Learning.

Systematic biology
Reconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and emp...