AIMC Topic: Whole Genome Sequencing
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A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.
Genome biology
May 23, 2025
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 ...
Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts.
The Journal of molecular diagnostics : JMD
Apr 29, 2025
Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. However, most focus on a single cancer hallmark, thus limiting effectiveness because of cancer's heterogen...
Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.
Scientific reports
Mar 26, 2025
Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, an...
ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.
Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Mar 18, 2025
Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial is...
A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.
Nature biomedical engineering
Mar 7, 2025
The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengt...
Machine learning detection of heteroresistance in Escherichia coli.
EBioMedicine
Feb 21, 2025
BACKGROUND: Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a susceptible main population contains small subpopulations of resistant cells. Mathematical models, ...
Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.
mBio
Jan 28, 2025
UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in computation. The unprecedented resolution of whole-genome typing is achieved by genotyping the vari...
Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.
BMC infectious diseases
Dec 5, 2024
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...
Antitubercular Agents
South Africa
Tuberculosis, Multidrug-Resistant
Whole Genome Sequencing
Mycobacterium tuberculosis
Isoniazid
Rifampin
Middle Aged
Humans
Microbial Sensitivity Tests
Machine Learning
Uganda
Drug Resistance, Bacterial
Polymorphism, Single Nucleotide
Young Adult
Female
Adult
Streptomycin
Male
Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.
Antimicrobial agents and chemotherapy
Nov 14, 2024
Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susce...
Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.
Scientific reports
Nov 7, 2024
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the dis...
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