从数据到治疗:对针对性治疗的多omics数据分析进行全面的探索
Arnab Mukherjee1, Suzanna Abraham1, Akshita Singh1
1Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Molecular biotechnology
|April 2, 2024
概括
多学科研究整合了各种生物数据,以揭示疾病机制. 机器学习分析这些大数据,以针对性治疗和改进诊断.
科学领域:
- 生物医学研究生物医学研究
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 药物发现正在转向通过分子分析了解疾病机制.
- 大数据,特别是异质的omics数据,正在彻底改变生物和生物医学研究.
- 多学科研究整合了多样化的数据层,以探索复杂的生物学相互关系.
研究的目的:
- 审查omics景观和相关的测试从基因组到代谢组.
- 突出机器学习 (ML) 在分析大规模数据中的作用.
- 探索多学科数据在完善疾病分类,诊断和向治疗方面的应用.
主要方法:
- 导航omics数据景观,从基因组学到代谢学.
- 利用机器学习算法进行复杂的数据分析.
- 应用网络药理学来建模疾病规范网络并验证目标.
主要成果:
- 欧米克斯数据集成完善了疾病分类,提高了诊断能力.
- 机器学习算法对于管理和解释大omics数据至关重要.
- 网络药理学方法改善了多个标和治疗潜力的验证.
结论:
- 多omics大数据方法正在改变生物研究和药物发现.
- 综合多样化的OMIC数据推动了针对性治疗的发展.
- 复杂的信息学,特别是机器学习,是释放多领域数据潜力的关键.
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