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Protein Attributes-Based Predictive Tool in a Down Syndrome Mouse Model: A Machine Learning Approach

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Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 747))

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Abstract

Down syndrome is a disorder caused by an imbalance in the 21 chromosome, affecting learning and memorizing abilities, which was shown to be improved with memantine administration. In this study we intent to determine the most relevant proteins that could play a role in learning ability, suitable for possible biomarkers and to evaluate the accuracy of several bioinformatic models as a predictive tool. Five different supervised learning models (K-NN, DT, SVM, NB, NN) were applied to the original database and the newly created ones from eight attribute weighting models. Model accuracies were calculated through cross validation. Nine proteins revealed to be strong candidates as future biomarkers and K-NN and Neural Network had the better overall performances and highest accuracies (86.26% ± 0.23%; 81.51% ± 0.48%), which makes them a promising predictive tool to study protein profiles in DS patients’ follow-up after treatment with memantine.

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Notes

  1. 1.

    GmbH R. Support Vector Machine - RapidMiner Documentation 2017.

  2. 2.

    GmbH R. Decision Tree - RapidMiner Documentation 2017.

  3. 3.

    GmbH R. Naive Bayes - RapidMiner Documentation 2017.

  4. 4.

    GmbH R. k-NN - RapidMiner Documentation 2017.

  5. 5.

    GmbH R. Neural Net - RapidMiner Documentation 2017.

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Acknowledgments

This study was funded by QVida+: Estimação Contínua de Qualidade de Vida para Auxílio Eficaz à Decisão Clínica, NORTE‐01‐0247‐FEDER‐003446, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and strategic project LIACC (PEst-UID/CEC/00027/2013).

Author information

Authors and Affiliations

  1. Escola Superior de Saúde, Instituto Politécnico do Porto, Rua Dr. António Bernardino de Almeida 431, 4249-015, Porto, Portugal

    Cláudia Ribeiro-Machado, Sara Costa Silva, Sara Aguiar & Brígida Mónica Faria

  2. i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Cláudia Ribeiro-Machado & Sara Costa Silva

  3. INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Cláudia Ribeiro-Machado

  4. IPATIMUP – Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Sara Costa Silva

  5. LIACC - Lab. Inteligência Artificial e Ciência de Computadores, Porto, Portugal

    Brígida Mónica Faria

Authors
  1. Cláudia Ribeiro-Machado
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  2. Sara Costa Silva
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  3. Sara Aguiar
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  4. Brígida Mónica Faria
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Corresponding authors

Correspondence to Cláudia Ribeiro-Machado , Sara Costa Silva or Sara Aguiar .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  3. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  4. DIMES, Universita della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Ribeiro-Machado, C., Silva, S.C., Aguiar, S., Faria, B.M. (2018). Protein Attributes-Based Predictive Tool in a Down Syndrome Mouse Model: A Machine Learning Approach. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-77700-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77699-6

  • Online ISBN: 978-3-319-77700-9

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