标题： 基于GPT-2 XL和NVIDIA H100的癫痫基因组分析深度学习流水线 显示英文标题

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

摘要： 癫痫是一种以反复发作的癫痫发作为特征的慢性神经系统疾病，全球患病人数估计达五千万人。 尽管高通量测序技术的进步使得对脑组织的广泛转录组分析成为可能，但这些高度复杂的数据显示解码仍然是一个挑战。 为解决这个问题，本文提出了一种新的分析流程，将深度学习策略与GPU加速计算相结合，以研究癫痫中的基因表达模式。 具体而言，我们提出的方法采用了GPT-2 XL，这是一种基于变压器的大型语言模型（LLM），具有15亿个参数，用于在基于Hopper架构的最新NVIDIA H100张量核心GPU上进行基因组序列分析。 我们提出的方法能够高效地预处理RNA序列数据、基因序列编码以及后续模式识别。 我们在两个癫痫数据集上进行了实验，包括GEO访问号GSE264537和GSE275235。 获得的结果揭示了几种显著的转录组变化，包括生酮饮食治疗后海马区星形胶质细胞增生的减少，以及斑马鱼癫痫模型中兴奋性-抑制性信号平衡的恢复。 此外，我们的结果突显了结合先进硬件加速器使用LLMs在神经疾病转录组表征中的有效性。 显示英文摘要

摘要： 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.