AIMC Topic: High-Throughput Nucleotide Sequencing

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Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

The Lancet. Oncology
BACKGROUND: Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-con...

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.

Scientific reports
AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

British journal of haematology
Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytogenetics and molecular biology) provides comprehensive diagnostic understandings. However, MBN subtypi...

Analysis of public perceptions on the use of artificial intelligence in genomic medicine.

Human genomics
PURPOSE: Next generation sequencing has led to the creation of large pools of genomic data with analysis rather than data generation now the limiting factor. Artificial intelligence (AI) may be required to optimize the benefits of these data, but lit...

Deep learning enables the use of ultra-high-density array in DNBSEQ.

Scientific reports
DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the t...

Interpretation knowledge extraction for genetic testing via question-answer model.

BMC genomics
BACKGROUND: Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next-generation sequencing (mNGS). The application of sequencing results to clinical diagnosis and treat...

Improving viral annotation with artificial intelligence.

mBio
Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environme...

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Functional & integrative genomics
Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large...

Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.

International journal of molecular sciences
With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we st...