构建灵活,可扩展和机器学习准备的多模式瘤学数据集
Aakash Tripathi1,2, Asim Waqas1,2, Kavya Venkatesan1
1Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
瘤学数据系统的多式集成 (MINDS) 统一了各种癌症数据,使精准医学成为可能. 这种可扩展的框架可以快速访问超过41,000个病例,支持先进的研究和个性化的患者护理.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算瘤学计算瘤学
- 数据科学数据科学数据科学
背景情况:
- 不同质的医疗数据的快速增长需要整合,以便全面了解疾病和优化治疗.
- 像癌症这样的复杂疾病需要整合的数据来实现精准医学和个性化疗法.
- 现有的生物医学数据孤岛限制了研究能力,并阻碍了先进分析模型的开发.
研究的目的:
- 提出瘤学数据系统 (MINDS) 的多模式集成,这是一个灵活,可扩展和具有成本效益的元数据框架.
- 从公共来源有效地将不同的数据融合到一个相互连接的,以患者为中心的框架中.
- 克服当前数据孤岛的局限性,并推进瘤学数据集成,用于研究和临床应用.
主要方法:
- 开发了MINDS,这是一个云原生元数据框架,用于整合多式模式瘤学数据.
- 从癌症研究数据共享 (CRDC) 等公共存储库中整合了超过41,000个病例.
- 实现了用于数据来源跟踪,自动缩放,访问控制和动态数据访问的功能.
主要成果:
- 整合了超过41,000个案例,从3.78PB的源数据中实现了高压缩比.
- 实现了5秒以下的查询响应时间,用于交互式数据探索.
- 为探索交叉数据类型关系和构建机器学习模型队列提供了一个接口.
结论:
- MINDS提供了一个可扩展,具有成本效益的解决方案,用于整合异构的瘤学数据,克服现有的孤岛.
- 该框架为研究人员提供了增强的分析能力,以发现诊断和预后见解.
- MINDS代表了向基于证据的个性化癌症护理和未来数据整合策略的重大进步.
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