AIMC Topic: High-Throughput Nucleotide Sequencing

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

British journal of haematology Dec 3, 2024
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...
Bone Marrow High-Throughput Nucleotide Sequencing Immunohistochemistry East Asian People Mutation Flow Cytometry CD5 Antigens Lymphoma, Mantle-Cell Leukemia, Hairy Cell Waldenstrom Macroglobulinemia Lymphoma, B-Cell Neprilysin Lymphoma, Follicular Humans Machine Learning Retrospective Studies Leukemia, Lymphocytic, Chronic, B-Cell
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Analysis of public perceptions on the use of artificial intelligence in genomic medicine.

Human genomics Nov 18, 2024
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...
Male Middle Aged Genomics Female Aged Adult Focus Groups Australia High-Throughput Nucleotide Sequencing Public Opinion Humans Artificial Intelligence
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Deep learning enables the use of ultra-high-density array in DNBSEQ.

Scientific reports Nov 13, 2024
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...
Deep Learning Image Processing, Computer-Assisted Sequence Analysis, DNA DNA High-Throughput Nucleotide Sequencing
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Interpretation knowledge extraction for genetic testing via question-answer model.

BMC genomics Nov 9, 2024
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...
High-Throughput Nucleotide Sequencing Metagenomics Computational Biology Genetic Testing Knowledge Bases Humans
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Improving viral annotation with artificial intelligence.

mBio Sep 4, 2024
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...
Genome, Viral Molecular Sequence Annotation Bacteriophages Metagenome Artificial Intelligence High-Throughput Nucleotide Sequencing Metagenomics Humans
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A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Functional & integrative genomics Aug 19, 2024
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...
High-Throughput Nucleotide Sequencing Humans Machine Learning Deep Learning
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Holistic understanding of trimethoprim resistance in using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning.

mBio Aug 9, 2024
UNLABELLED: Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to...
Genome, Bacterial Trimethoprim Resistance Microbial Sensitivity Tests Genome-Wide Association Study Mutation Streptococcus pneumoniae Trimethoprim High-Throughput Nucleotide Sequencing Pneumococcal Infections Humans Machine Learning Anti-Bacterial Agents
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Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.

International journal of molecular sciences Aug 4, 2024
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...
Algorithms Deep Learning Genomics Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Saccharomyces cerevisiae Neural Networks, Computer Software Schizosaccharomyces Genome, Fungal Neurospora crassa
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Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

PLoS computational biology Jul 2, 2024
The classification of B cell lymphomas-mainly based on light microscopy evaluation by a pathologist-requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are importan...
Humans B-Lymphocytes Machine Learning High-Throughput Nucleotide Sequencing Algorithms Lymphoma, B-Cell Receptors, Antigen, B-Cell Leukemia, Lymphocytic, Chronic, B-Cell Lymphoma, Large B-Cell, Diffuse Computational Biology
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Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

International journal of molecular sciences Jun 20, 2024
Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (E...
Exosomes High-Throughput Nucleotide Sequencing Circulating MicroRNA Middle Aged Gene Expression Profiling Extracellular Vesicles Computational Biology MicroRNAs Ischemic Stroke Female Aged Male Humans Machine Learning Biomarkers
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