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

  • Cláudia Ribeiro-Machado
  • Sara Costa Silva
  • Sara Aguiar
  • Brígida Mónica Faria
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 747)

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.

Keywords

Down syndrome Prediction Learning improvement Attributes weighting Data mining Classification 

Notes

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).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Escola Superior de Saúde, Instituto Politécnico do PortoPortoPortugal
  2. 2.i3S - Instituto de Investigação e Inovação em SaúdeUniversidade do PortoPortoPortugal
  3. 3.INEB - Instituto de Engenharia BiomédicaUniversidade do PortoPortoPortugal
  4. 4.IPATIMUP – Instituto de Patologia e Imunologia Molecular da Universidade do PortoPortoPortugal
  5. 5.LIACC - Lab. Inteligência Artificial e Ciência de ComputadoresPortoPortugal

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