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A Bayesian Network Model for the Parkinson’s Disease: A Study of Gene Expression Levels

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Current Trends in Semantic Web Technologies: Theory and Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 815))

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremor, postural instability, bradykinesia and rigidity. It is the second most prevalent neurodegenerative disease in the world. Currently, no cure has been found. Multiple investigations have proposed environmental and genetic factors, but no one has been determined as the trigger for the development of this disease, so it is a public health challenge in our society characterized by the increase of elderly people. The use of machine learning techniques has increased in the medical field and to help solve biological problems. The unprecedented volume of biomedical data provides a great opportunity for better understanding, prediction and decision making of conditions. In this study, we modeled gene expression profiles of peripheral blood samples from 105 individuals (50 with PD, 33 with control of neurodegenerative diseases, other than PD, and 22 healthy controls) using Bayesian networks with different dimensionality reduction techniques to create several sets of genes. From the obtained sets, classification models were generated and some genes that could be considered as PD candidates were obtained and some genes previously reported with this disease were corroborated.

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Acknowledgements

The authors thankfully acknowledge the computer resources, technical expertise and support provided by the Laboratorio Nacional de Supercómputo del Sureste de México, CONACYT network of national laboratories.

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Authors and Affiliations

  1. Facultad de Ciencias Químicas, Universidad Veracruzana, Circuito Universitario Gonzalo Aguirre Beltrán S/N, Zona Universitaria, CP 91000, Xalapa, Veracruz, Mexico

    Sonia Lilia Mestizo-Gutiérrez

  2. Laboratorio Nacional de Informática Avanzada, Av. Enrique C. Rébsamen 80, Centro, CP 91090, Xalapa, Veracruz, México

    Joan Arturo Jácome-Delgado

  3. Centro de Investigación en Inteligencia Artificial, Universidad Veracruzana, Sebastián Camacho No. 5, Col. Centro, CP 91000, Xalapa, Veracruz, México

    Viviana Yarel Rosales-Morales & Nicandro Cruz-Ramírez

  4. Centro de Investigaciones Cerebrales, Universidad Veracruzana, Av. Luis Castelazo Ayala S/N, CP 91190, Xalapa, Veracruz, México

    Gonzalo Emiliano Aranda-Abreu

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  1. Sonia Lilia Mestizo-Gutiérrez
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  2. Joan Arturo Jácome-Delgado
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  3. Viviana Yarel Rosales-Morales
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  4. Nicandro Cruz-Ramírez
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  5. Gonzalo Emiliano Aranda-Abreu
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Correspondence to Sonia Lilia Mestizo-Gutiérrez .

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Editors and Affiliations

  1. Division of Research and Postgraduate Studies, Tecnológico Nacional de México/I.T. Orizaba, Orizaba, Mexico

    Giner Alor-Hernández

  2. Division of Research and Postgraduate Studies, CONACYT-Instituto Tecnológico de Orizaba, Orizaba, Mexico

    José Luis Sánchez-Cervantes

  3. Centro de Tecnología Biomédica (CTB), Universidad Politécnica de Madrid, Pozuelo de Alarcon, Madrid, Spain

    Alejandro Rodríguez-González

  4. Facultad de Informática, Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

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Mestizo-Gutiérrez, S.L., Jácome-Delgado, J.A., Rosales-Morales, V.Y., Cruz-Ramírez, N., Aranda-Abreu, G.E. (2019). A Bayesian Network Model for the Parkinson’s Disease: A Study of Gene Expression Levels. In: Alor-Hernández, G., Sánchez-Cervantes, J., Rodríguez-González, A., Valencia-García, R. (eds) Current Trends in Semantic Web Technologies: Theory and Practice. Studies in Computational Intelligence, vol 815. Springer, Cham. https://doi.org/10.1007/978-3-030-06149-4_7

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