A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models
A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models
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DOI：
10.1186/s13062-019-0249-6
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发表时间：
2019-11-21
期刊：
影响因子：
5.5
通讯作者：
Vassilev, Dimitar
中科院分区：
文献类型：
--
作者：
Mihaylov, Iliyan;Kandula, Maciej;Vassilev, Dimitar
背景近年来，高通量技术与临床试验一起被大量用于研究各种类型的癌症。在这种大规模研究中产生的数据是异质的，有不同的类型和格式。由于缺乏有效的集成策略，新的模型是必要的有效和可操作的数据集成，其中临床和分子信息可以有效地加入存储，访问和易用性。这些模型与机器学习方法相结合，可以准确预测癌症研究中的生存时间，可以对疾病发展产生新的见解，并导致精确的个性化治疗。我们开发了一种方法，用于两种癌症数据集（乳腺癌和神经母细胞瘤）的智能数据集成-在CAMDA 2018年“癌症数据集成研讨会”中提供，并比较了预测生存时间的模型。我们开发了一种新的基于语义网络的数据集成框架，该框架利用NoSQL数据库，在那里我们结合了临床和表达谱数据，同时使用原始数据记录和外部知识源。利用综合数据，我们引入了肿瘤综合临床特征（TICF）-一种用于准确预测患者生存时间的新特征。最后，我们应用并验证了几种用于生存时间预测的机器学习模型。结论：我们开发了一个框架，用于临床和组学数据的语义整合，可以跨多个癌症研究借用信息。通过将数据与外部领域知识源联系起来，我们的方法可以通过发现内部关系来丰富所研究的数据。提出并验证的用于生存时间预测的机器学习模型产生了准确的结果。
Background Recently high-throughput technologies have been massively used alongside clinical tests to study various types of cancer. Data generated in such large-scale studies are heterogeneous, of different types and formats. With lack of effective integration strategies novel models are necessary for efficient and operative data integration, where both clinical and molecular information can be effectively joined for storage, access and ease of use. Such models, combined with machine learning methods for accurate prediction of survival time in cancer studies, can yield novel insights into disease development and lead to precise personalized therapies. Results We developed an approach for intelligent data integration of two cancer datasets (breast cancer and neuroblastoma) - provided in the CAMDA 2018 'Cancer Data Integration Challenge', and compared models for prediction of survival time. We developed a novel semantic network-based data integration framework that utilizes NoSQL databases, where we combined clinical and expression profile data, using both raw data records and external knowledge sources. Utilizing the integrated data we introduced Tumor Integrated Clinical Feature (TICF) - a new feature for accurate prediction of patient survival time. Finally, we applied and validated several machine learning models for survival time prediction. Conclusion We developed a framework for semantic integration of clinical and omics data that can borrow information across multiple cancer studies. By linking data with external domain knowledge sources our approach facilitates enrichment of the studied data by discovery of internal relations. The proposed and validated machine learning models for survival time prediction yielded accurate results.