AIMC Topic: Phylogeny
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Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to c...
Deep Learning from Phylogenies for Diversification Analyses.
Systematic biology
Dec 30, 2023
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 ...
Fusang: a framework for phylogenetic tree inference via deep learning.
Nucleic acids research
Nov 10, 2023
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
Nov 1, 2023
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...
Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.
Genome biology and evolution
Jun 1, 2023
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...
DEPP: Deep Learning Enables Extending Species Trees using Single Genes.
Systematic biology
May 19, 2023
Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phy...
Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.
Briefings in bioinformatics
Jan 19, 2023
LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classif...
Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.
Yi chuan = Hereditas
Nov 20, 2022
Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic clas...
The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database.
Molecular biology and evolution
Oct 7, 2022
The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (Alph...
DeePVP: Identification and classification of phage virion proteins using deep learning.
GigaScience
Aug 11, 2022
BACKGROUND: Many biological properties of phages are determined by phage virion proteins (PVPs), and the poor annotation of PVPs is a bottleneck for many areas of viral research, such as viral phylogenetic analysis, viral host identification, and ant...
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