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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Jun 30, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100.

Muhammad Omer Latif, Hayat Ullah, Muhammad Ali Shafique

    Arxiv
    |November 19, 2025
    PubMed
    Summary

    This study introduces a novel deep learning pipeline for analyzing gene expression in epilepsy. The method uses advanced AI and GPU computing to uncover key transcriptomic changes, offering new insights into neurological disease mechanisms.

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    Area of Science:

    • Neuroscience
    • Genomics
    • Computational Biology

    Background:

    • Epilepsy affects 50 million globally, presenting complex transcriptomic data challenges.
    • High-throughput sequencing generates vast datasets requiring sophisticated analysis.

    Purpose of the Study:

    • To develop and validate a deep learning pipeline for analyzing gene expression patterns in epilepsy.
    • To leverage Large Language Models (LLMs) and GPU acceleration for efficient transcriptomic analysis.

    Main Methods:

    • Utilized GPT-2 XL, a 1.5 billion parameter LLM, for genomic sequence analysis.
    • Employed NVIDIA H100 Tensor Core GPUs for accelerated computation.
    • Developed a pipeline for RNA sequence data preprocessing, gene encoding, and pattern identification.

    Main Results:

    • Identified significant transcriptomic modifications in epilepsy datasets (GSE264537, GSE275235).
    • Observed reduced hippocampal astrogliosis following ketogenic diet treatment.
    • Found restored excitatory-inhibitory signaling balance in a zebrafish epilepsy model.

    Conclusions:

    • LLMs combined with hardware acceleration offer an effective approach for transcriptomic characterization in neurological diseases.
    • The pipeline facilitates deeper understanding of gene expression in epilepsy.
    • Findings contribute to advancing research in epilepsy and related neurological conditions.