通过深度学习方法对多主题数据集成进行审查,以诊断疾病,预后和治疗
Jael Sanyanda Wekesa1, Michael Kimwele1
1School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.
Frontiers in genetics
|August 7, 2023
概括
深度学习模型通过分析多omics数据来加快疾病诊断,减少时间和成本. 这些计算方法的进步对于准确的疾病预测和治疗至关重要.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 人工智能的人工智能
背景情况:
- 准确的疾病诊断对于有效的治疗和管理至关重要.
- 聚合酶链反应 (PCR) 是一种标准的分子诊断方法.
- 深度学习 (DL) 越来越多地用于识别与疾病相关的基因,帮助诊断,预后和治疗.
研究的目的:
- 系统地审查使用深度学习用于疾病预测的多omics数据分析的最新趋势.
- 突出现场当前的挑战,并讨论DL进步的重要性.
主要方法:
- 系统评估最近的深度学习技术,应用于多学科数据.
- 对用于检测疾病,包括癌症的计算方法的分析.
主要成果:
- 深度学习模型显示,与传统的湿实验室程序相比,它有望减少时间和成本.
- 复杂的计算方法有助于检测复杂的疾病,如癌症.
- 使用DL进行多omics数据分析是疾病预测的一个关键趋势.
结论:
- 深度学习方法的进步和优化对于克服疾病预测方面的挑战至关重要.
- 开发用于数据集成的新型深度学习方法对于疾病检测和治疗至关重要.
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