AIMC Topic: Whole Genome Sequencing
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Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.
PeerJ
Oct 9, 2024
BACKGROUND: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pat...
Semi-supervised machine learning method for predicting homogeneous ancestry groups to assess Hardy-Weinberg equilibrium in diverse whole-genome sequencing studies.
American journal of human genetics
Sep 12, 2024
Large-scale, multi-ethnic whole-genome sequencing (WGS) studies, such as the National Human Genome Research Institute Genome Sequencing Program's Centers for Common Disease Genomics (CCDG), play an important role in increasing diversity for genetic r...
Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.
Scientific reports
Sep 3, 2024
Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their stan...
Cell Line, Tumor
Triple Negative Breast Neoplasms
Cell Proliferation
DNA Copy Number Variations
Machine Learning
Female
Reproducibility of Results
Feasibility Studies
DNA Repair
Predictive Learning Models
Chromosomal Instability
Area Under Curve
Neoplastic Cells, Circulating
Whole Genome Sequencing
Humans
A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.
PLoS genetics
Aug 26, 2024
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rat...
Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.
PLoS computational biology
Aug 5, 2024
There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration w...
AI-based histopathology image analysis reveals a distinct subset of endometrial cancers.
Nature communications
Jun 26, 2024
Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molec...
Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.
Food research international (Ottawa, Ont.)
Jun 19, 2024
Salmonella is a foodborne pathogen that causes salmonellosis, of which retail chicken meat is a major source. However, the prevalence of Salmonella in different retail chicken supply modes and the threat posed to consumers remains unclear. The preval...
Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.
Nature medicine
Jun 14, 2024
In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (T...
Fragmentomics features of ovarian cancer.
International journal of cancer
May 20, 2024
Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an adv...
Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth.
BMC genomics
May 14, 2024
BACKGROUND: The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequenci...
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