AIMC Topic: High-Throughput Nucleotide Sequencing
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Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity.
Frontiers in cellular and infection microbiology
Apr 15, 2025
OBJECTIVE: Metagenomic next-generation sequencing (mNGS) can potentially detect various pathogenic microorganisms without bias to improve the diagnostic rate of fever of unknown origin (FUO), but there are no effective methods to predict mNGS-positiv...
Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions.
Science advances
Apr 11, 2025
Using millions of methylation segments, we developed DiffuCpG, a generative artificial intelligence (AI) diffusion model designed to solve the critical challenge of missing data in high-throughput methylation technologies. DiffuCpG goes beyond conven...
T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.
Frontiers in immunology
Apr 8, 2025
BACKGROUND: T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their variations across digestive system cancers are not well understood. Characterizing TCR differences between colorectal cancer (CRC) and gastric cancer (GC)...
GONNMDA: A Ordered Message Passing GNN Approach for miRNA-Disease Association Prediction.
Genes
Apr 1, 2025
Small non-coding molecules known as microRNAs (miRNAs) play a critical role in disease diagnosis, treatment, and prognosis evaluation. Traditional wet-lab methods for validating miRNA-disease associations are often time-consuming and inefficient. Wit...
An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives.
Computers in biology and medicine
Mar 6, 2025
UNLABELLED: Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due to their rapid spread and significant morbidity and mortality rates. Next-generation sequencing (NGS...
EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs.
International journal of biological macromolecules
Mar 6, 2025
Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA sequencing (HTlncRNAs) has identified tens of thousands of lncRNAs across species, but only a small fra...
Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.
Human genomics
Feb 23, 2025
BACKGROUND: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these a...
A Perspective on Artificial Intelligence for Molecular Pathologists.
The Journal of molecular diagnostics : JMD
Feb 13, 2025
The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The huge quantities of data generated by sequencing, the enormous complexity of human and microbial gene...
Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.
Journal of cancer research and clinical oncology
Feb 9, 2025
INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 (HER2) is closely related to their prognosis and treatment decisions. This study aimed to further improve the accuracy and efficiency of HER2 amplific...
An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging...
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