AIMC Topic: Whole Genome Sequencing

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Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens.

Communications biology
Variations in host species significantly impact bacterial growth traits and antibiotic resistance, making it essential to consider host origin when evaluating the zoonotic potential of pathogens. This study focuses on multiple Brucella species, which...

Non-coding genetic elements of lung cancer identified using whole genome sequencing in 13,722 Chinese.

Nature communications
A substantial portion of lung cancer-associated genetic elements in East Asian populations remains unidentified, underscoring the need for large-scale genome-wide studies, particularly on non-coding regulation. We conducted a whole genome sequencing ...

Whole genome resequencing reveals genetic diversity, population structure, and selection signatures in local duck breeds.

BMC genomics
BACKGROUND: Shandong's local duck breeds are renowned for their outstanding egg-laying performance and are regarded as valuable assets within China's waterfowl germplasm. Understanding the genetic characteristics of these populations, along with moni...

Chronological age estimation from human microbiomes with transformer-based Robust Principal Component Analysis.

Communications biology
Deep learning for microbiome analysis has shown potential for understanding microbial communities and human phenotypes. Here, we propose an approach, Transformer-based Robust Principal Component Analysis(TRPCA), which leverages the strengths of trans...

Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BMC genomic data
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.

Microbiology spectrum
Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) to guide timely treatments. Traditional culture-based AST methods are slow, while existing whole-gen...

Genome sequencing is critical for forecasting outcomes following congenital cardiac surgery.

Nature communications
While exome and whole genome sequencing have transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, its utility to predict clinical outcomes remains understudied. Here, we use artificial intelligence ...

Early detection of emerging SARS-CoV-2 Variants from wastewater through genome sequencing and machine learning.

Nature communications
Genome sequencing from wastewater enables accurate and cost-effective identification of SARS-CoV-2 variants. However, existing computational pipelines have limitations in detecting emerging variants not yet characterized in humans. Here, we present a...

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.

Genome biology
Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from ...