Journal
BIOLOGY DIRECT
Volume 14, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13062-019-0249-6
Keywords
Breast cancer; Neuroblastoma; Semantic data integration; Machine learning; Survival time prediction
Categories
Funding
- grant GloBIG: A Model of Integration of Cloud Framework for Hybrid Massive Parallelism and its Application for Analysis and Automated Semantic Enhancement of Big Heterogeneous Data Collections [DN02/9]
- Universities for Science, Informatics and Technologies in the e-Society (UNITe) - Operational Program Science and Education for Smart Growth [BG05M2OP001-1.001-0004]
- European Regional Development Fund
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available