Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer’s Disease

Patel, Hamel; Iniesta, Raquel; Ståhl, Daniel; Dobson, Richard; Newhouse, Stephen

doi:10.1101/621987

Cited by 3 publications

(3 citation statements)

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“…The results showed that testing the serum samples offered promising results with an AUC (area under the receiver operating characteristic curve) of 0.77 [ 41 ]. Moreover, a recent study that applied a typical approach of training machine learning algorithms using the public gene database from 160 AD and 127 healthy controls produced models with an average sensitivity of 48.7% (95% CI = 34.7–64.6) [ 42 ]. Our study applied LDA to reduce dimensionality and extract features from the multiplex blood biomarkers and then distinguished individual disease subgroups using the RF classifier, which provided an average accuracy of 76% for the AD and PD spectrums, as well as FTD.…”

Section: Discussionmentioning

confidence: 99%

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Lin

Chiu

Chen

et al. 2020

IJMS

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Easily accessible biomarkers for Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.

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Section: Discussionmentioning

confidence: 99%

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Lin

Chiu

Chen

et al. 2020

IJMS

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“…Omics mRNA data have also been analyzed to produce diagnostic biosignatures. Patel et al using an XGBoost classification algorithm reported two mRNA-biosignatures, a 57-mRNA biosignature for the discrimination between AD patients and healthy individuals with 0.450 AUC and an 89 mRNA-biosignature for the discrimination between AD patients and non-AD patients (consisted of either healthy individuals or patients with other diseases) with AUC 0.860 [ 26 ]. Lee and Lee produced three mRNA-biosignatures via logistic regression, L1-regularized logistic regression, random forest, support vector machine, and deep neural network using three independent datasets reaching AUC 0.657, 0.874, and 0.804, respectively.…”

Section: Discussionmentioning

confidence: 99%

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning

Karaglani

Gourlia

Tsamardinos

et al. 2020

JCM

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Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

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“…Utilizing this level, the diseases can be detected and the best treatment options and alterations in other processes can be discovered [17]. In this direction, bloodderived gene expression biomarkers in [18] are used to differentiate AD cases from N cases. XGBoost is used as a classifier and successfully identifies AD by including associated mental and geriatric health issues.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

El-Gawady¹,

Tawfik²,

Makhlouf³

2023

Computers, Materials &Amp; Continua

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Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer's disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are preprocessed identically. In the GS phase, a hybrid filtering method and embedded method are used. In the classification phase, three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase. The final phase is to validate the performance of these classifiers using different metrics. The crux of this article is to select the most informative genes from the hybrid method, and the best ML technique to predict AD using this minimal set of genes. Five different datasets are used to achieve our goal. We predict AD with impressive values for MultiLayer Perceptron (MLP) classifier which has the best performance metrics in four datasets, and the Support Vector Machine (SVM) achieves the highest performance values in only one dataset. We assessed the classifiers using seven metrics; and received impressive results, allowing for a credible performance rating. The metrics values we obtain in our study lie in the range [.97, .99] for the accuracy (Acc), [.97, .99] for F1score, [.94, .98] for kappa index, [.97, .99] for area under curve (AUC), [.95, 1] for precision, [.98, .99] for sensitivity (recall), and [.98, 1] for specificity. With these results, the proposed approach outperforms recent interesting results. With these results, the proposed approach outperforms recent interesting results.

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Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer’s Disease

Cited by 3 publications

References 50 publications

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

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