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Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

Research output: Contribution to journalArticlepeer-review

Simon Lam, Muhammad Arif, Xiya Song, Mathias Uhlén, Adil Mardinoglu

Original languageEnglish
Article number889728
JournalFrontiers in Molecular Neuroscience
Volume15
DOIs
Published31 May 2022

Bibliographical note

Funding Information: This work was financially supported by Knut and Alice Wallenberg Foundation (Grant No. 2017.0303) to AM. Funding Information: This research has been conducted using the UK Biobank Resource under application number 64488. We acknowledge use of the research computing facility at King's College London, Rosalind (https://rosalind.kcl.ac.uk, accessed 2021-06-01). Publisher Copyright: Copyright © 2022 Lam, Arif, Song, Uhlén and Mardinoglu.

King's Authors

Abstract

It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.

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