AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Whole Genome Sequencing

Showing 21 to 30 of 67 articles

Clear Filters

Deciphering complex antibiotic resistance patterns in through whole genome sequencing and machine learning.

Frontiers in cellular and infection microbiology
INTRODUCTION: Helicobacter pylori (H.pylori, Hp) affects billions of people worldwide. However, the emerging resistance of Hp to antibiotics challenges the effectiveness of current treatments. Investigating the genotype-phenotype connection for Hp us...

Classification of non-coding variants with high pathogenic impact.

PLoS genetics
Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a prote...

Building a model for predicting metabolic syndrome using artificial intelligence based on an investigation of whole-genome sequencing.

Journal of translational medicine
BACKGROUND: The circadian system is responsible for regulating various physiological activities and behaviors and has been gaining recognition. The circadian rhythm is adjusted in a 24-h cycle and has transcriptional-translational feedback loops. Whe...

Haplotype and population structure inference using neural networks in whole-genome sequencing data.

Genome research
Accurate inference of population structure is important in many studies of population genetics. Here we present HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phas...

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

Genome medicine
BACKGROUND: Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NI...

Improving variant calling using population data and deep learning.

BMC bioinformatics
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to ...

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

International journal of molecular sciences
The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a wa...

Automating the Illumina DNA library preparation kit for whole genome sequencing applications on the flowbot ONE liquid handler robot.

Scientific reports
Whole-genome sequencing (WGS) is currently making its transition from research tool into routine (clinical) diagnostic practice. The workflow for WGS includes the highly labor-intensive library preparations (LP), one of the most critical steps in the...

Fragmentomics features of ovarian cancer.

International journal of cancer
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
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...