AIMC Topic: High-Throughput Nucleotide Sequencing

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DNA forensics at forty: the way forward.

International journal of legal medicine
Forensic DNA analysis has transformed criminal investigations since its inception in 1985. Over four decades, this field has evolved through various phases-from the early stages of exploration to today's highly sophisticated methodologies. Key advanc...

Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Scientific reports
Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with c...

Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein-Barr virus (EBV)-associated, genomically stable (GS), and chromosomally instable (CIN)....

The Role of Artificial Intelligence in Identifying Gene Variants and Improving Diagnosis.

Genes
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the gene, typically diagnosed during early childhood and characterized by significant phenotypic heterogeneity. Despite advancements in next-generation sequencin...

Unraveling the three-dimensional genome structure using machine learning.

BMB reports
The study of chromatin interactions has advanced considerably with technologies such as high-throughput chromosome conformation capture (Hi-C) sequencing, providing a genome-wide view of physical interactions within the nucleus. These techniques have...

DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.

Briefings in bioinformatics
The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability of NGS datasets have led to a significant surge in biomedical research. To better understand the molecular processes, underlying cancer and to support...

COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive d...

Machine learning-optimized targeted detection of alternative splicing.

Nucleic acids research
RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that gr...

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.

Briefings in functional genomics
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing...