Abstract
Multiple sclerosis (MS) is a complex disease affecting the central nervous system, mainly in young adults. Even though there is no cure for this disease, correct phenotype identification on time will help the clinicians to select proper treatment option, which can delay the severity progression and the long-term disability progression of the disease. As automated phenotype identification system can improve the situation in a positive way, this study explores the role of exon level transcriptome data in the prediction of three different phenotypes of this disease: subgroups, therapy option for individual patient and duration of the disease. Features selected from the downloaded transcriptome data using feature importance and mutual information are used along with different machine learning models to choose the best one with the highest accuracy. Comparison between models shows that while classifying the patients into their corresponding subgroups this study achieves the best accuracy value of 0.88 ± 0.05. In the prediction of therapy option our accuracy is 0.94 ± 0.07and in disease duration prediction the root mean square value is 4.1 ± 1.4. Notably, all the highest accuracies obtained here are using same machine learning algorithms and the value shows that they can be used in clinical practices.
Similar content being viewed by others
Data availability
Datasets used in this study is publicly available in GEO data repository. All the models use pre-built functions in python libraries, none of them are implemented here from the scratch.
Code availability
Python libraries and standard methods are used in this study. Code can be shared via Github, if required.
References
Ghasemi N, Razavi S, Nikzad E (2017) Multiple sclerosis: pathogenesis, symptoms, diagnoses and cell-based therapy. Cell J 19(1):1–10
Huang WJ, Chen WW, Xia Z (2017) Multiple sclerosis: pathology, diagnosis and treatments. Exp Ther Med 13(6):3163–3166
Phu CH, Shepherd R (2010) Multiple sclerosis. In: Carr JH, Shepherd RB (eds) Neurological rehabilitation: optimizing motor performance, 2nd edn. Elsevier-Health Sciences Division, London, pp 335–347
Goldenberg MM (2012) Multiple sclerosis review. P & T Peer-Rev J Formul Manag 37(3):175–184
Chong V, Tan C (2008) A review of multiple sclerosis with Asian perspective. Med J Malays 63:356–361
McGinley MP, Goldschmidt CH, Rae-Grant AD (2021) Diagnosis and treatment of multiple sclerosis: a review. JAMA 325(8)
Lublin F, Reingold S, Cohen J, Cutter G, Sørensen P, Thompson A, Wolinsky J, Balcer L, Banwell B, Barkhof F, Bebo BJ, Calabresi P, Clanet M, Comi G, Fox R, Freedman M, Goodman A, Inglese M, Kappos L, Kieseier B, Lincoln J, Lubetzki C, Miller AE (2014) Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 83(3):278–286
Hecker M, Koczan D, Zettl U The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE190847. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190847
Hurwitz BJ (2009) The diagnosis of multiple sclerosis and the clinical subtypes. Ann Indian Acad Neurol 12(4):226–230
Polman C, Reingold S, Banwell B, Clanet M, Cohen J, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin F, Montalban X, O’Connor P, Sandberg-Wollheim M, Thompso A, Waubant E, Weinshenker B, Wolinsky J (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302
Carroll William M (2018) 2017 McDonald MS diagnostic criteria: evidence-based revisions. Mult Scler J 24(2)
Hauser Stephen L, Goodin Douglas S, Hauser Stephen L, Josephson S Andrew (2018) Multiple sclerosis and other demyelinating diseases. In: Harrison's Neurology in Clinical Medicine, 4e.
Bradshaw Michael, Houtchens Maria (2018) Neurology Board Review: Multiple Sclerosis
Karaca Yeliz, Cattani Carlo (2017) Clustering multiple sclerosis subgroups with multifractal methods and self-organizing map algorithm. Fractals 25(4)
Moazami Faezeh, Lefevre-Utile Alain, Papaloukas Costas, Soumelis Vassili (2021) Machine learning approaches in study of multiple sclerosis disease through magnetic resonance images. Front Immunol 12, 700582
Eshaghi A, Young AL, Wijeratne PA, Prados F, Arnold DL, Narayanan S, Guttmann CRG, Barkhof F, Alexander DC, Thompson AJ, Chard D, Ciccarelli O (2021) Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 12(1), 2078
Darvishi S, Hamidi O, Poorolajal J (2021) Prediction of multiple sclerosis disease using machine learning classifiers: a comparative study. J Prev Med Hyg 62(1) E192-E199.
Hecker M, Fitzner B, Boxberger N, Putscher E (2023) Transcriptome alterations in peripheral blood B cells of patients with multiple sclerosis receiving immune reconstitution therapy. J Neuroinflammation 20, 181 (2023)
Dobson R, Giovannoni G (2019) Multiple sclerosis - a review. Eur J Neurol 26(1):27–40
Catherine S, Brittany H, Caylin I, Eden F, Megan C (2020) Cipollone and Victoria, A current understanding of multiple sclerosis. JAAPA 33(2):19–23
Victoria B, Michelle J, Mona S (2012) Multiple sclerosis: a comprehensive review for the physician assistant. JAAPA 25(8):24–29
Mohamed K, Koriem M (2016) Multiple sclerosis: new insights and trends. Asian Pac J Trop Biomed 6(5):429–440
Hauser S, Cree B (2020) Treatment of multiple sclerosis: a review. Am J Med 133(12):1380–1390
Dargahi N, Katsara M, Tselios T, Androutsou M, Courten Ed, Matsoukas J, Apostolopoulos V (2017) Multiple sclerosis: immunopathology and treatment update. Brain Sci 7(7):78
Alam Afroj, Muqeem Mohd (2023) An optimal heart disease prediction using chaos game optimization-based recurrent neural model. Int J Inform Technol 1-8
Pratheeba J, Bandara K, Nayanajith Y (2024) Protein data in the identification and stage prediction of bronchopulmonary dysplasia on preterm infants: a machine learning study. Int J Inform Technol, 16(1).
Zabian Arwa, Ibrahim Ahmed Zohair(2024) Karnauph classifier for predicting breast cancer based on morphological features. Int J Inform Technol, 16(1)
Agrawal Sneha, Sahu Satya Prakash (2024) Image-based Parkinson disease detection using deep transfer learning and optimization algorithm. Int J Inform Technol, 16(2)
Lavanya KG, Dhanalakshmi P, Nandhini M (2023) Computerized segmentation of MR brain tumor: an integrated approach of multi-modal fusion and unsupervised clustering. Int J Inform Technol, 16(2)
Rangarajan Prasanna Kumar, Gurusamy Bharathi Mohan, Rajasekar Elakkiya, Ippatapu Venkata Srisurya, Chereddy Spandana (2023) Retroactive data structure for protein–protein interaction in lung cancer using Dijkstra algorithm. Int J Inform Technol, 16(2)
Vijay M, Puja C, Murtaza T, Ashish S (2019) Personalized medicine: an overview. Int J Pharm Qual Assur 10:290–294
Wenting H, Owen C, Xianta J, Syamala B, Caitlin JN, Maria CW, Amber LC, Michelle P (2022) Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway. Biomed Eng Online 21, 21 (2022)
Edgar RPDL-S, Omar AD-R, Ana MH-N, Juvenal R-R, Carlos P-O, Jorge DM-S (2023) A deep learning approach for predicting multiple sclerosis. Micromachines 14(4):749
Eitel F, Soehler E, Bellmann-Strobl J, Brandt AU, Ruprecht K, Giess RM, Kuchling J, Asseyer S, Weygandt M, Haynes J-D, Scheel M, Paul F, Ritter K (2019) Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation. NeuroImage Clin 24:102003
Zurita M, Montalba C, Labbé T, Cruz JP, Rocha JDd, Tejos C, Ciampi E, Cárcamo C, Sitaram R, Uribe S (2018) Characterization of relapsing–remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data. NeuroImage Clin 20, 724–730
Adrian M, Gabriel K, Claudio S, Diana MS, Françoise D-D, Sabine VH, Dominique S-M (2017) Machine Learning Approach for classifying multiple sclerosis courses by combining Clinical Data with Lesion loads and magnetic resonance metabolic features. Front NeuroSci 11;11:398
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Author information
Authors and Affiliations
Contributions
This is a single author paper.
Corresponding author
Ethics declarations
Conflict of interest
There are no conflict of interest between authors. This is a single author paper
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jeyananthan, P. Machine learning in the identification of phenotypes of multiple sclerosis patients. Int. j. inf. tecnol. 16, 2307–2313 (2024). https://doi.org/10.1007/s41870-024-01735-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-024-01735-y