AIMC Topic: Whole Genome Sequencing

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Noninvasive fetal genotyping using deep neural networks.

Briefings in bioinformatics
Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. In obstetrics, maternal plasma cfDNA have already proven its utility, enabling noninvasive prenatal ...

Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Studies in health technology and informatics
Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment o...

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

Bioinformatics (Oxford, England)
MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuber...

A deep learning approach for filtering structural variants in short read sequencing data.

Briefings in bioinformatics
Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accurate detection of structural variants is a challenging task. Especially existing structural variant ...

Pathogenic potential assessment of the Shiga toxin-producing by a source attribution-considered machine learning model.

Proceedings of the National Academy of Sciences of the United States of America
Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)-based method using whole-genome sequencing ...

[Application of the artificial intelligence-rapid whole-genome sequencing diagnostic system in the neonatal/pediatric intensive care unit].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics
Pediatric patients in the neonatal intensive care unit (NICU) and the pediatric intensive care unit (PICU) have a high incidence rate of genetic diseases, and early rapid etiological diagnosis and targeted interventions can help to reduce mortality o...

Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Escherichia coli is a species of bacteria that can be present in a wide variety of mammalian hosts and potentially soil environments. E. coli has an open genome and can show considerable diversity in gene content between isolates. It is a reasonable ...

A guide to machine learning for bacterial host attribution using genome sequence data.

Microbial genomics
With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generated from both diagnostic and research laboratories for the foreseeable future, then there is both an o...

Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics.

Current protocols in human genetics
The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnost...