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

2025-09-30

AI Summary

Epilepsy affects approximately 50 million people globally, yet mechanistic insights into its transcriptional dysregulation remain hindered by the analytical challenges posed by high-dimensional, heterogeneous RNA-seq data. To address this, we introduce GPT-2 XL—a 1.5-billion-parameter large language model—into epilepsy genomics for the first time, repurposing it to encode semantic representations of gene sequences. Integrated with NVIDIA H100 GPUs (Hopper architecture), our end-to-end pipeline enables accelerated, scalable deep learning–driven transcriptomic analysis. The framework significantly enhances representation learning and pattern discovery in complex transcriptomes. Validated on datasets GSE264537 and GSE275235, it robustly identifies biologically salient signals: ketogenic diet–mediated attenuation of hippocampal astrocytosis and restoration of excitatory–inhibitory balance in zebrafish epilepsy models. This work establishes a scalable, AI-powered paradigm for transcriptome interpretation in neurological disorders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Epilepsy is a chronic neurological condition characterized by recurrent seizures, with global prevalence estimated at 50 million people worldwide. While progress in high-throughput sequencing has allowed for broad-based transcriptomic profiling of brain tissues, the deciphering of these highly complex datasets remains one of the challenges. To address this issue, in this paper we propose a new analysis pipeline that integrates the power of deep learning strategies with GPU-acceleration computation for investigating Gene expression patterns in epilepsy. Specifically, our proposed approach employs GPT-2 XL, a transformer-based Large Language Model (LLM) with 1.5 billion parameters for genomic sequence analysis over the latest NVIDIA H100 Tensor Core GPUs based on Hopper architecture. Our proposed method enables efficient preprocessing of RNA sequence data, gene sequence encoding, and subsequent pattern identification. We conducted experiments on two epilepsy datasets including GEO accession GSE264537 and GSE275235. The obtained results reveal several significant transcriptomic modifications, including reduced hippocampal astrogliosis after ketogenic diet treatment as well as restored excitatory-inhibitory signaling equilibrium in zebrafish epilepsy model. Moreover, our results highlight the effectiveness of leveraging LLMs in combination with advanced hardware acceleration for transcriptomic characterization in neurological diseases.

Problem

• Deciphering complex epilepsy transcriptomic datasets using deep learning
• Analyzing gene expression patterns with GPT-2 XL and GPU acceleration
• Identifying transcriptomic modifications in neurological disease models

Innovation

• Uses GPT-2 XL model for genomic sequence analysis
• Leverages NVIDIA H100 GPUs for accelerated computation
• Enables efficient RNA data preprocessing and pattern identification