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良好的机器学习的方法与多omics数据的方法

Thibaud Coroller¹, Berkman Sahiner², Anup Amatya³

¹Novartis Pharmaceutical Company, East Hanover, New Jersey, USA.

Clinical pharmacology and therapeutics

|November 15, 2023

概括

诺华和FDA在为期四年的项目中合作,使用先进的分析方法发现了转移性乳腺癌的新放射基因组因素. 这项研究为未来的整合人工智能和机器学习的多学科项目提供了指导方针.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
基因组学就是基因组学.
数据科学数据科学数据科学

背景情况:

诺华制药公司与美国食品和药物管理局 (FDA) 之间的为期四年的合作于2020年开始.
该项目侧重于新的数据模式和先进的分析.
主要的科学问题是确定HR+/HER-转移性乳腺癌的预后和预测因素.

研究的目的:

为涉及跨机构的多学科团队的多主题项目提供切实可见的指导方针.
分享通过利用人工智能 (AI) 和机器学习 (ML) 的合作项目获得的见解.
为实施探索性数据科学项目提供可操作的指导.

主要方法:

探索基于放射基因组学的新预后和预测因素.
应用高级分析,包括人工智能和机器学习.
通过四个关键步骤对多主题项目的结构化方法:计划,设计,开发和传播.

主要成果:

已经产生了有价值的见解,以促进未来的科学项目.
展示了整合复杂数据模式和高级分析的潜力.
提供了有效沟通的实际策略和协作研究中的良好数据科学实践.

结论:

这次合作为转移性乳腺癌的放射基因组学提供了重要的见解.
制定的指导方针为成功的多学科研究努力提供了一个框架.
有效的沟通和强大的数据科学实践对于机构间的科学项目至关重要.

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Proteomics

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