Integrating Multiple Immunogenetic Data Sources For Feature Extraction and Mining Mutation Patterns: The Case of Chronic Lymphocytic Leukemia Shared Mutations

I. Kavakiotis, A. Xochelli, A. Agathangelidis, G. Tsoumakas, N. Maglaveras, K. Stamatopoulos, A. Hadzidimitriou, I. Vlahavas, I. Chouvarda, “Integrating Multiple Immunogenetic Data Sources For Feature Extraction and Mining Mutation Patterns: The Case of Chronic Lymphocytic Leukemia Shared Mutations”, Heraklion, Crete, Greece, November 10-12, 2014.

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Author(s): I. Kavakiotis, A. Xochelli, A. Agathangelidis, Grigorios Tsoumakas, N. Maglaveras, K. Stamatopoulos, A. Hadzidimitriou, I. Vlahavas, I. Chouvarda

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Appeared In: Statistical Methods for Omics Data Integration and Analysis. Heraklion, Crete, Greece, November 10-12, 2014.

Keywords: data integration, feature extraction, list aggregation, mutation patterns, somatic hypermutation, SHM, chronic lymphocytic leukemia, CLL.

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2014, Agathangelidis, Chouvarda, Hadtzidimitriou, Kavakiotis, Maglaveras, Stamatopoulos, Tsoumakas, Vlahavas, Xochelli

Abstract: The aim of this work is to extract features and create high quality datasets through integration of multiple information resources for somatic hypermutation (SHM) analysis in the clonotypic immunoglobulin (IG) receptors of patients with Chronic Lymphocytic Leukemia (CLL). This can set the basis for an in-depth investigation of a series of as yet unanswered biological questions, through data mining analysis, which is clinically relevant given the great prognostic value of SHM in CLL (Damle et al, 1999). The virtue of the proposed approach is illustrated via the case of “towards analysis” which is our attempt to identify potential developmental transformation or movement of IG gene germlines towards other IG gene germlines through SHM.