AIMC Topic: Phylogeny
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Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.
The New phytologist
Sep 7, 2023
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...
ModelRevelator: Fast phylogenetic model estimation via deep learning.
Molecular phylogenetics and evolution
Aug 16, 2023
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, ...
Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.
Viruses
Jul 15, 2023
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
Jul 6, 2023
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...
Supervised learning and model analysis with compositional data.
PLoS computational biology
Jun 30, 2023
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...
On the fractal patterns of language structures.
PloS one
May 18, 2023
Natural Language Processing (NLP) makes use of Artificial Intelligence algorithms to extract meaningful information from unstructured texts, i.e., content that lacks metadata and cannot easily be indexed or mapped onto standard database fields. It ha...
PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences.
Journal of bioinformatics and computational biology
Feb 10, 2023
This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suita...
Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life.
Scientific reports
Feb 6, 2023
In this study, we investigate how an organism's codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build severa...
Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.
Microbiology spectrum
Dec 13, 2022
The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus in...
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics.
PloS one
Dec 7, 2022
Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of dat...
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