ArticleJournal of molecular neuroscience : MN2020
Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes.
Article in Journal of molecular neuroscience : MN, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 31 citations in OpenAlex.
- The Prospective Regulatory Functions of lncRNAs and Their ceRNA Networks in the Development of Motor Neurons and Associated Diseases.Biomolecules · 2026Review
- Evaluating genetic-based disease prediction approaches through simulation.Human genetics · 2026Article
- From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.BMC genomics · 2025Article
- Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.International journal of molecular sciences · 2025Article
- Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS).Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence in peritoneal dialysis: general overview.Renal failure · 2022Article
- Article
- Small GTPases of the Rab and Arf Families: Key Regulators of Intracellular Trafficking in Neurodegeneration.International journal of molecular sciences · 2021Review
- Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.Frontiers in digital health · 2021Article
- Developing a clinical-environmental-genotypic prognostic index for relapsing-onset multiple sclerosis and clinically isolated syndrome.Brain communications · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.