Abstract
A typical biological study includes analysis of heterogeneous biological databases, e.g., genomics, proteomics, metabolomics, and microarray gene expression. These datasets correlate at the patient-level, e.g., decrease in the workload of a group of genes in body cells increases the work of other group and raises the number of their products. Joint analysis of correlated patient-level data sources improves the final diagnosis. State-of-art biological methods, such as differential expression analysis, do not support heterogeneous data source integration and analysis. Recently, scientists in different computational fields have made significant improvements in classical algorithms for data integration to enable investigation of different data types at the same level. Applying these methods on biological data gives more insight into associating diseases with heterogeneous groups of patients. In this paper, we improve upon our previous study and propose the use of a combination of a data reduction technique and similarity network analysis (SNF) as a scalable mechanism for integrating new biological data types. We demonstrated our approach by analyzing the risk factors of Acute Myeloid Leukemia (AML) patients when multiple data sources are presented and uncover new correlations between patients and patient survival time.
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Samimi, H., Tešić, J., Ngu, A.H.H. (2019). Patient Centric Data Integration for Improved Diagnosis and Risk Prediction. In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2019 2019. Lecture Notes in Computer Science(), vol 11721. Springer, Cham. https://doi.org/10.1007/978-3-030-33752-0_13
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DOI: https://doi.org/10.1007/978-3-030-33752-0_13
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