Abstract
In this article, we present four distance-based discrimination functions for the identification of relevant genomic segments that distinguish between two groups of data. These discrimination functions are designed for the detection of genomic regions responsible for disease. One of them was previously employed for the analysis of the Human Papilloma Virus family in relation to carcinogenicity (Diallo et al. 2009). Here, we used an improved version of the algorithm described in Badescu et al. (2008) and Diallo et al. (2009) for analyzing the information content of a multiple sequence alignments (MSA) in relation to epidemiologic data. In this study, those functions have been applied to identify specific genomic regions responsible for the hyperinvasivity of Neisseria Meningitidis. Neisseria Meningitidis is a major causal agent of meningitis and septicaemia worldwide. This study suggests that the tested functions permit to identify relevant regions and known molecular features. We found that one of the new functions tested is specifically well correlated with surface-exposed loops, regions important in vaccine design.
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Badescu, D., Diallo, A.B., Makarenkov, V. (2010). Identification of Specific Genomic Regions Responsible for the Invasivity of Neisseria Meningitidis . In: Locarek-Junge, H., Weihs, C. (eds) Classification as a Tool for Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10745-0_53
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DOI: https://doi.org/10.1007/978-3-642-10745-0_53
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