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Pattern Recognition Applied to the Analysis of Genomic Data and Its Association to Diseases

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Pattern Recognition Techniques Applied to Biomedical Problems

Abstract

The analysis of genomic data has been used to generate information about genetic variants and expression patterns correlated with specific physical traits. In the last decades, these analyses have evolved toward analyzing thousands of entities at the same time. Moreover, these analyses have produced an enormous amount of biomedical literature reporting associations between genes and diseases. In this scenario, pattern recognition techniques have been truly useful, so a review of how these techniques have been applied is relevant. Thus, in this chapter we present a brief introduction to the high-throughput sequencing methodologies. Then, we describe the process of identification of genomic variants and genetic expression profiles that have been used for the diagnostic of diseases, followed by a general overview of the gene-disease association extraction from biomedical literature.

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Authors and Affiliations

  1. Instituto de Biotecnologia de la Universidad Nacional Autónoma de México, Cuernavaca Morelos, C.P., 62210, México

    Verónica Jiménez-Jacinto

  2. Instituto Nacional de Medicina Genómica, Ciudad de México, CDMX, México

    Laura Gómez-Romero

  3. Centro de Ciencias Genómicas de la Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México

    Carlos-Francisco Méndez-Cruz

Authors
  1. Verónica Jiménez-Jacinto
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  2. Laura Gómez-Romero
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  3. Carlos-Francisco Méndez-Cruz
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Correspondence to Verónica Jiménez-Jacinto .

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Editors and Affiliations

  1. Electrical Engineering Department, Universidad Autónoma Metropolitana-Iztapalapa, Mexico City, Mexico

    Martha Refugio Ortiz-Posadas

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Jiménez-Jacinto, V., Gómez-Romero, L., Méndez-Cruz, CF. (2020). Pattern Recognition Applied to the Analysis of Genomic Data and Its Association to Diseases. In: Ortiz-Posadas, M. (eds) Pattern Recognition Techniques Applied to Biomedical Problems. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. https://doi.org/10.1007/978-3-030-38021-2_2

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