使用基因组序列处理模型进行疾病鉴定的研究和分析:实证审查
Sony K Ahuja1, Deepti D Shrimankar1, Aditi R Durge1
1Visvesvaraya National Institute of Technology, Computer Science and Engineering, India.
Current genomics
|January 3, 2024
概括
为基因组序列分析选择机器学习模型是具有挑战性的,因为性能各不相同. 这份调查详细介绍了模型的细微差别,帮助研究人员选择疾病预测和临床应用的最佳解决方案.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 人类基因序列为疾病预测提供了全面的健康信息.
- 机器学习模型对于分析基因组序列以识别各种疾病至关重要.
- 现有的模型在准确性,可扩展性和性能方面存在权衡.
研究的目的:
- 为了调查和比较不同的机器学习模型用于基因组序列处理.
- 根据特定的应用和性能需求,为选择最佳模型提供一个框架.
- 帮助基因组系统设计人员确定适合临床场景的模型.
主要方法:
- 对基因组数据的机器学习模型的详细调查.
- 分析每个模型的功能细微差别,优势和局限性.
- 基于准确性,延迟,精度,可扩展性和部署成本的模型的定量比较.
主要成果:
- 模型在性能特征上有很大差异,例如速度与准确性和可扩展性.
- 为模型评估引入了一种新的基因组处理效率排名 (GPER).
- 定量数据有助于对实时临床应用的模型进行比较.
结论:
- 这项调查为基因组处理模型的景观提供了关键的见解.
- 研究人员可以利用详细的比较和GPER来选择合适的模型.
- 有信息的模型选择可以提高基因组数据的疾病预测的效率和准确性.
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