Abstract：
Advanced technology innovations allow cost-effective, high-throughput profiling of biological systems. It enabled genome sequencing in days using advanced technologies (e.g., next-generation sequencing, microarrays, and mass spectrometry). Since technology has been developed, massive biological data (e.g., genomics, proteomics) has been produced cheaply, allowing the \"big data\" era to create new opportunities to solve medical and biological complications in many disciplines-preventive medicine, biology, Personalized Medicine, gene sequencing, healthcare, and industry. Computational biology and bioinformatics are interdisciplinary fields that develop and apply computational methods (e.g., analytical methods, mathematical modeling, and simulation) to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples, to make new predictions or discover new biology. Biological data storage, mining, and analysis have challenges because data is much more heterogeneous. In this study, the big data resources of genomics, proteomics, and metabolomics have been explored to solve biological problems using big data analysis approaches. The goal is to build a network of relationship-based gene-disease associations to prioritize phenotypes common to epilepsy and seizure disease. Through network analysis, The 10 seed genes, 22 associated genes, 132 microRNAs, and 38 transcription factors have been identified that have a direct effect on all forms of epilepsy and seizures. The majority of seed genes, according to the results of a functional analysis of seed genes, are involved in the acetylcholine-gated channel complex (10%) and the heterotrimeric G-protein complex (10%) pathways related to cellular components, followed by a role in the regulation of action potential (20%) and positive regulation of vascular endothelial growth factor production (20%) in Epilepsy and Seizures pathways related to biological processes. This study might provide insight into the workings of the disease and shows the importance of continued research into epilepsy and other conditions that can trigger seizure activity.
摘要：
先进的技术创新允许具有成本效益，生物系统的高通量分析。它使用先进技术在几天内实现了基因组测序(例如，下一代测序，微阵列,和质谱）。自从技术发展以来，大量生物数据(例如，基因组学，蛋白质组学)生产成本低，允许“大数据”时代创造新的机会来解决许多学科的医学和生物并发症-预防医学，生物学个性化医学,基因测序，healthcare,和工业。计算生物学和生物信息学是发展和应用计算方法的跨学科领域(例如，分析方法，数学建模,和模拟)来分析大量的生物数据，比如基因序列，细胞群，或者蛋白质样本,做出新的预测或发现新的生物学。生物数据存储,采矿,和分析有挑战，因为数据更加异构。在这项研究中,基因组学的大数据资源，蛋白质组学，和代谢组学已经探索使用大数据分析方法来解决生物学问题。目标是建立一个基于关系的基因-疾病关联网络，以优先考虑癫痫和癫痫发作疾病常见的表型。通过网络分析,10个种子基因，22个相关基因，132microRNAs,38个转录因子对所有形式的癫痫和癫痫发作有直接影响。大多数种子基因，根据种子基因的功能分析结果，参与与细胞成分相关的乙酰胆碱门控通道复合物（10％）和异源三聚体G蛋白复合物（10％）途径，其次是在癫痫和癫痫发作通路相关的生物过程中，动作电位的调节（20％）和血管内皮生长因子的产生的正调节（20％）。这项研究可能会深入了解这种疾病的运作方式，并表明继续研究癫痫和其他可能引发癫痫发作的疾病的重要性。
