AIMC Topic: High-Throughput Nucleotide Sequencing

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The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

Journal of biopharmaceutical statistics Oct 11, 2023
The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly dilute...
Liquid Biopsy Circulating Tumor DNA Mutation High-Throughput Nucleotide Sequencing Machine Learning Algorithms Biomarkers, Tumor Genotype Computer Simulation Humans
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Improving variant calling using population data and deep learning.

BMC bioinformatics May 12, 2023
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 ...
Genome-Wide Association Study Polymorphism, Single Nucleotide Whole Genome Sequencing Humans Deep Learning Gene Frequency High-Throughput Nucleotide Sequencing Genome, Human
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A scoping review on deep learning for next-generation RNA-Seq. data analysis.

Functional & integrative genomics Apr 21, 2023
In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfol...
Sequence Analysis, RNA High-Throughput Nucleotide Sequencing RNA-Seq Genomics Algorithms Reproducibility of Results Deep Learning
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Validation of genetic variants from NGS data using deep convolutional neural networks.

BMC bioinformatics Apr 20, 2023
Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessa...
Machine Learning Reproducibility of Results High-Throughput Nucleotide Sequencing Humans Neural Networks, Computer
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Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

Scientific reports Mar 28, 2023
With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly develop...
Receptors, Antigen, T-Cell Humans High-Throughput Nucleotide Sequencing Algorithms Cytomegalovirus Cytomegalovirus Infections Artificial Intelligence Adult
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DeltaMSI: artificial intelligence-based modeling of microsatellite instability scoring on next-generation sequencing data.

BMC bioinformatics Mar 1, 2023
BACKGROUND: DNA mismatch repair deficiency (dMMR) testing is crucial for detection of microsatellite unstable (MSI) tumors. MSI is detected by aberrant indel length distributions of microsatellite markers, either by visual inspection of PCR-fragment ...
Microsatellite Repeats Microsatellite Instability High-Throughput Nucleotide Sequencing Artificial Intelligence Machine Learning Humans
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Single-cell RNA-seq data analysis based on directed graph neural network.

Methods (San Diego, Calif.) Feb 16, 2023
Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and comple...
Neural Networks, Computer Cluster Analysis Single-Cell Analysis Data Analysis Single-Cell Gene Expression Analysis High-Throughput Nucleotide Sequencing Gene Expression Profiling Sequence Analysis, RNA
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A deep learning approach reveals unexplored landscape of viral expression in cancer.

Nature communications Feb 11, 2023
About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses ...
Deep Learning High-Throughput Nucleotide Sequencing Genome, Viral Viruses Humans Neoplasms
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Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics Jan 27, 2023
BACKGROUND: Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replicati...
Genomic Structural Variation Software Genomics High-Throughput Nucleotide Sequencing Sequence Analysis, DNA Genome, Human Unsupervised Machine Learning Humans
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Symphonizing pileup and full-alignment for deep learning-based long-read variant calling.

Nature computational science Dec 19, 2022
Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles...
High-Throughput Nucleotide Sequencing Deep Learning Polymorphism, Single Nucleotide
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