AIMC Topic: High-Throughput Nucleotide Sequencing

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Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.

Nature cancer Jan 29, 2025
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tum...
Gene Expression Profiling Transcriptome Deep Learning Biomarkers, Tumor Brain Neoplasms Male DNA Methylation High-Throughput Nucleotide Sequencing Central Nervous System Neoplasms Humans Female
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Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.

Genome research Jan 22, 2025
The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation mo...
Humans Artificial Intelligence Machine Learning Cell-Free Nucleic Acids High-Throughput Nucleotide Sequencing Liquid Biopsy Female Neoplasms
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A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing.

Nature communications Jan 2, 2025
Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Previous work has demonstrated that next-generation sequencing data can be used to ...
High-Throughput Nucleotide Sequencing Neoplasms Humans Deep Learning Neural Networks, Computer Microsatellite Instability Algorithms Software Prospective Studies Biomarkers, Tumor
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High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.

IEEE transactions on nanobioscience Jan 2, 2025
Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was intro...
Humans Neural Networks, Computer Sequence Analysis, DNA DNA High-Throughput Nucleotide Sequencing Algorithms Deep Learning Computational Biology
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Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites.

Methods (San Diego, Calif.) Dec 30, 2024
5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cos...
Computational Biology RNA High-Throughput Nucleotide Sequencing Humans Animals 5-Methylcytosine Neural Networks, Computer Deep Learning Mice
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Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.

BMC bioinformatics Dec 24, 2024
BACKGROUND: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hid...
Exome Sequencing DNA Copy Number Variations High-Throughput Nucleotide Sequencing Data Analysis Algorithms Humans Sequence Analysis, DNA Neural Networks, Computer
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The Role of ctDNA in the Management of Non-Small-Cell Lung Cancer in the AI and NGS Era.

International journal of molecular sciences Dec 20, 2024
Liquid biopsy (LB) involves the analysis of circulating tumour-derived DNA (ctDNA), providing a minimally invasive method for gathering both quantitative and qualitative information. Genomic analysis of ctDNA through next-generation sequencing (NGS) ...
Carcinoma, Non-Small-Cell Lung Circulating Tumor DNA Prognosis High-Throughput Nucleotide Sequencing Humans Liquid Biopsy Biomarkers, Tumor Artificial Intelligence Lung Neoplasms
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Comparative Analysis of Generative Pre-Trained Transformer Models in Oncogene-Driven Non-Small Cell Lung Cancer: Introducing the Generative Artificial Intelligence Performance Score.

JCO clinical cancer informatics Dec 11, 2024
PURPOSE: Precision oncology in non-small cell lung cancer (NSCLC) relies on biomarker testing for clinical decision making. Despite its importance, challenges like the lack of genomic oncology training, nonstandardized biomarker reporting, and a rapi...
Precision Medicine Biomarkers, Tumor Lung Neoplasms Carcinoma, Non-Small-Cell Lung High-Throughput Nucleotide Sequencing Oncogenes Humans Artificial Intelligence
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Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

The Lancet. Oncology Dec 6, 2024
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...
High-Throughput Nucleotide Sequencing Male Middle Aged Deep Learning China Humans Lung Neoplasms Prognosis Mutation Retrospective Studies Female Aged Biomarkers, Tumor
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AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.

Scientific reports Dec 5, 2024
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...
Genomics Computational Biology Alzheimer Disease High-Throughput Nucleotide Sequencing Machine Learning Software Algorithms Humans
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