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Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes

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Abstract

The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sclerosis (MS) patients and 390 healthy subjects. Single nucleotide polymorphisms (SNPs) within ANRIL (rs1333045, rs1333048, rs4977574 and rs10757278), EVI5 (rs6680578, rs10735781 and rs11810217), ACE (rs4359 and rs1799752), MALAT1 (rs619586 and rs3200401), GAS5 (rs2067079 and rs6790), H19 (rs2839698 and rs217727), NINJ2 (rs11833579 and rs3809263), GRM7 (rs6782011 and rs779867), VLA4 (rs1143676), CBLB (rs12487066) and VEGFA (rs3025039 and rs2071559) had been genotyped in all individuals. We used “Keras” package for generation and training the ANN model. The k-folds cross-validation strategy was applied to confirm model generalization and overfit prevention. The locally interpretable model-agnostic explanation (LIME) was applied to explain model predictions at the data sample level. The TT genotype of the GAS5 rs2067079 had the most protective effect against MS, while the DD genotype of the ACE rs1799752 was the most prominent risk genotype. The accuracy, sensitivity and specificity values of the model were 64.73%, 64.95% and 64.49% respectively. The ROC AUC value was 69.67%. The current study is a preliminary study to appraise the application of ANN for prediction of risk of MS based on genomic data.

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Funding

The present article is financially supported by Research Department of the School of Medicine, Shahid Beheshti University of Medical Sciences.

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Authors and Affiliations

  1. Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Soudeh Ghafouri-Fard & Mir Davood Omrani

  2. Urogenital Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mohammad Taheri

  3. School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

    Amir Daaee

  4. Student of Dentistry, Dental School, Shahid Beheshti University of Medical Science, Tehran, Iran

    Hossein Mohammad-Rahimi

Authors
  1. Soudeh Ghafouri-Fard
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  2. Mohammad Taheri
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  3. Mir Davood Omrani
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  4. Amir Daaee
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  5. Hossein Mohammad-Rahimi
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Contributions

MT designed and supervised the study. HMR and AD performed the data analyses. SGF and MDO wrote and revised the manuscript. All authors read the manuscript drafts, contributed edits and approved the final manuscript.

Corresponding authors

Correspondence to Mohammad Taheri or Mir Davood Omrani.

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Conflict of Interest

The authors declare that they have no competing interests.

Ethics Approval and Consent to Participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval was obtained from the ethical committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.MSP.REC.1397.1022).

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The analysed data sets generated during the study are available from the corresponding author on reasonable request.

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Ghafouri-Fard, S., Taheri, M., Omrani, M.D. et al. Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes. J Mol Neurosci 70, 1081–1087 (2020). https://doi.org/10.1007/s12031-020-01514-x

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  • DOI: https://doi.org/10.1007/s12031-020-01514-x

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