Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

Lam, Simon; Arif, Muhammad; Song, Xiya; Uhlén, Mathias; Mardinoğlu, Adil

doi:10.3389/fnmol.2022.889728

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…The SHapley Additive exPlanations (SHAP) method allowed for assessing the impact of each factor on the outcome, making the results more interpretable and quantification. Supervise ML, the multinomial model has also successfully identified diagnostic biomarkers for neurological disorders, including MG, using big biological data such as genotyping, blood, and urine biochemistry data ( Lam et al, 2022 ). During the COVID-19 pandemic, ML algorithms were utilized for telemedicine in MG, analyzing eye or body motions and vocalization for standardized data acquisition and real-time feedback ( Garbey et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis

Chang,

Liu,

et al. 2023

Front. Microbiol.

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Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

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Section: Discussionmentioning

confidence: 99%

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis

Chang,

Liu,

et al. 2023

Front. Microbiol.

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“…In epilepsy and intellectual disability cases, DOMINO accurately identified known genes and predicted new candidates. An ML study on genotyping and clinical data from neurological disease patients developed a multinomial linear model that accurately identified 88% of disease samples, emphasizing the importance of age and cognition [33]. This analysis also found common SNPs across neurological diseases, linking MND to RBBP5 and TNF, and MG to oncogenes and brain-related genes.…”

Section: Genotype-phenotype Integrationmentioning

confidence: 91%

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

Alganmi

2024

BioMedInformatics

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Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain.

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“…Pathology is the core medical discipline to bring proteomics to patients via a new class of “postgenomic” protein-directed assays for early disease detection, risk prediction, choice of therapy and combination therapies, and surveillance. Combined with improvements in instrument design, orthogonal , and multiomic approaches, − rapid progress in advanced computational capabilities including AI and machine learning ,− can now facilitate the development of personalized/precision medicine for the improvement of human health. ,, …”

Section: Pathology Pillarmentioning

confidence: 99%

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1–4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

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Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

Cited by 8 publications

References 29 publications

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

The 2023 Report on the Proteome from the HUPO Human Proteome Project

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