大数据分析的新兴趋势 计算生物学中的大数据分析和健康信息学中的生物信息学:和发作的案例研究
Usha Chouhan1, Rakesh Kumar Sahu2, Shaifali Bhatt1
1Department of Mathematics, Bioinformatics & Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.
Methods in molecular biology (Clifton, N.J.)
|October 6, 2023
概括
这项研究使用了大数据分析来识别关键基因,微RNA和转录因子,这些因子都与和有关. 这些发现突出了诸如乙胆通道和G蛋白信号传递等途径,为疾病机制提供了洞察力.
科学领域:
- 计算生物学和生物信息学
- 基因组学,蛋白质组学和代谢组学.
- 医疗保健中的大数据分析.
背景情况:
- 先进的技术使成本效益高,高通量生物系统概况成为可能.
- 大数据的时代,由基因组学和蛋白质组学推动,为医学和生物学进步提供了机会.
- 分析大型,异构的生物数据集带来了大量的存储,挖掘和分析挑战.
研究的目的:
- 探索大数据资源 (基因组学,蛋白质组学,代谢组学) 以解决生物学问题.
- 建立基因疾病协会网络,以优先考虑和发作表型.
- 为了确定影响和的关键分子参与者.
主要方法:
- 利用大数据分析方法对基因组学,蛋白质组学和代谢组学数据进行分析.
- 采用网络分析来建立基因疾病关联网络.
- 对已识别的种子基因进行功能分析.
主要成果:
- 确定了10个种子基因,22个关联基因,132个微RNA和38个影响和的转录因子.
- 功能分析显示,种子基因主要参与乙胆通道和异构三蛋白G蛋白复合体通路.
- 涉及的关键生物过程包括作用潜力的调节和血管内皮生长因子的产生.
结论:
- 这项研究提供了对和发作背后的分子机制的关键见解.
- 确定了分子参与者和途径,为未来的研究和治疗策略提供了潜在的目标.
- 强调使用大数据方法对和相关疾病的持续调查的重要性.
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