AIMC Topic: Phylogeny

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Morphological traits and machine learning for genetic lineage prediction of two reef-building corals.

PloS one
Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plas...

Genome-wide annotation and comparative analysis of miniature inverted-repeat transposable elements (MITEs) in six pear species.

Planta
Through multi-faceted comparative analysis of MITEs across six pear genomes, we revealed their distribution patterns, functional impacts and their significant role as genomic origins for miRNAs, with copy number being the most critical factor for MIT...

LucaPCycle: Illuminating microbial phosphorus cycling in deep-sea cold seep sediments using protein language models.

Nature communications
Phosphorus is essential for life and critically influences marine productivity. Despite geochemical evidence of active phosphorus cycling in deep-sea cold seeps, the microbial processes involved remain poorly understood. Traditional sequence-based se...

Genome evolution of Kaposi sarcoma-associated herpesvirus (KSHV).

Journal of virology
UNLABELLED: Kaposi sarcoma (KS) is the most common cancer in people living with HIV (PLWH), particularly in sub-Saharan Africa (SSA), where Kaposi sarcoma herpesvirus (KSHV or human herpesvirus 8 [HHV-8]) is endemic. In KSHV endemic areas, the overal...

Microbiome determinants of productivity in aquaculture of whiteleg shrimp.

Applied and environmental microbiology
UNLABELLED: Aquaculture holds immense promise for addressing the food needs of our growing global population. Yet, a quantitative understanding of the factors that control its efficiency and productivity has remained elusive. In this study, we addres...

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

Genome biology
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to in...

Equitable machine learning counteracts ancestral bias in precision medicine.

Nature communications
Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. Therapeutics and outcomes remain hidden because we lack insights that could be gained from analyzing...

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
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...

Decoding the blueprint of receptor binding by filoviruses through large-scale binding assays and machine learning.

Cell host & microbe
Evidence suggests that bats are important hosts of filoviruses, yet the specific species involved remain largely unidentified. Niemann-Pick C1 (NPC1) is an essential entry receptor, with amino acid variations influencing viral susceptibility and spec...

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Environment international
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts...