生物工程/蛋白质酶作为抗癌试剂的设计,采用集成的Omics和机器学习方法
Weimin Zuo1,2, Hang Fai Kwok3,4,5
1Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR, China.
Methods in molecular biology (Clifton, N.J.)
|December 1, 2023
概括
开发新的抗癌 (ACP) 是至关重要的. 这项研究将omics数据与机器学习 (ML) 结合起来,以经济高效地从自然来源发现有力的ACP,克服传统的实验限制.
科学领域:
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
- 在瘤学瘤学.
背景情况:
- 癌症对全球健康造成重大负担,需要创新的治疗策略.
- 现有的癌症治疗方法面临诸多挑战,包括副作用和耐药性.
- 抗癌 (ACP) 具有作为强效,特异性和低毒性治疗剂的潜力.
研究的目的:
- 为发现抗癌 (ACP) 提出一种新的,具有成本效益的战略.
- 详细介绍omics数据与机器学习 (ML) 的整合,用于非洲国家和地区的识别.
- 探索天然产品作为新型抗癌疗法的来源的潜力.
主要方法:
- 收集和分析来自自然来源 (如毒素) 的转录组和蛋白组数据.
- 机器学习 (ML) 算法的应用,用于预测ACP活动的建模.
- 描述性方法指导基于ML的新型抗癌的发现.
主要成果:
- 展示综合的OMICS和ML作为一个强大的方法,以ACP的发现.
- 与传统的实验方法相比,突出了效率和成本效益.
- 从天然产品数据中识别潜在的新型阿克巴候选国.
结论:
- 集成的奥米克和ML为发现新型抗癌提供了可行和有效的策略.
- 这种方法加速了从天然产品中识别有前途的治疗候选药物.
- 描述的方法有助于开发下一代癌症疗法.
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