scholarly journals Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

2021 ◽  
Author(s):  
Ziyi Li ◽  
Xiaoqian Jiang ◽  
Yizhuo Wang ◽  
Yejin Kim
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Treatment Strategies ◽  
Drug Repurposing ◽  
Modern Technology ◽  
Disease Classification ◽  
Future Research ◽  
New Biomarkers

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

scholarly journals Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Bing Bai ◽  
David Vanderwall ◽  
Yuxin Li ◽  
Xusheng Wang ◽  
Suresh Poudel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rna Splicing ◽  
Biomarker Discovery ◽  
Meta Analysis ◽  
Treatment Strategies ◽  
Cell Types ◽  
Synaptic Function ◽  
Mitochondrial Activity ◽  
Cellular Markers

AbstractMass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer’s disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in “amyloidome” (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.

scholarly journals A metabolite-based machine learning approach to diagnose Alzheimer’s-type dementia in blood: Results from the European Medical Information Framework for Alzheimer’s Disease biomarker discovery cohort

2019 ◽  
Author(s):  
Daniel Stamate ◽  
Min Kim ◽  
Petroula Proitsi ◽  
Sarah Westwood ◽  
Alison Baird ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medical Information ◽  
Biomarker Discovery ◽  
Gradient Boosting ◽  
Plasma Metabolites ◽  
Csf Biomarkers ◽  
Extreme Gradient Boosting ◽  
And Gender

AbstractINTRODUCTIONMachine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer’s Disease (AD). Here we set out to test the performance of metabolites in blood to categorise AD when compared to CSF biomarkers.METHODSThis study analysed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n=883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV).RESULTSOn the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively.DISCUSSIONThis study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders

scholarly journals Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease

2020 ◽  
Author(s):  
Steve Rodriguez ◽  
Clemens Hug ◽  
Petar Todorov ◽  
Nienke Moret ◽  
Sarah A. Boswell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Drug Repurposing ◽  
Molecular Entity ◽  
Braak Stage ◽  
Negative Results ◽  
Innate Immune Signaling ◽  
And Function

AbstractClinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their “off-targets”, revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

scholarly journals Neuroinflammation and Alzheimer’s Disease: A Machine Learning Approach to CSF Proteomics

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1930
Author(s):  
Lorenzo Gaetani ◽  
Giovanni Bellomo ◽  
Lucilla Parnetti ◽  
Kaj Blennow ◽  
Henrik Zetterberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Neurological Diseases ◽  
Univariate Analysis ◽  
Learning Approach ◽  
Barrier Dysfunction ◽  
Csf Analysis ◽  
Machine Learning Approach

In Alzheimer’s disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although not clearly quantifiable by means of fluid biomarkers. We aimed to identify quantifiable protein biomarkers reflecting neuroinflammation in AD using multiplex proximity extension assay (PEA) testing. Cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment due to AD (AD-MCI) and from controls, i.e., patients with other neurological diseases (OND), were analyzed with the Olink Inflammation PEA biomarker panel. A machine-learning approach was then used to identify biomarkers discriminating AD-MCI (n: 34) from OND (n: 25). On univariate analysis, SIRT2, HGF, MMP-10, and CXCL5 showed high discriminatory performance (AUC 0.809, p = 5.2 × 10−4, AUC 0.802, p = 6.4 × 10−4, AUC 0.793, p = 3.2 × 10−3, AUC 0.761, p = 2.3 × 10−3, respectively), with higher CSF levels in AD-MCI patients as compared to controls. These same proteins were the best contributors to the penalized logistic regression model discriminating AD-MCI from controls (AUC of the model 0.906, p = 2.97 × 10−7). The biological processes regulated by these proteins include astrocyte and microglia activation, amyloid, and tau misfolding modulation, and blood-brain barrier dysfunction. Using a high-throughput multiplex CSF analysis coupled with a machine-learning statistical approach, we identified novel biomarkers reflecting neuroinflammation in AD. Studies confirming these results by means of different assays are needed to validate PEA as a multiplex technique for CSF analysis and biomarker discovery in the field of neurological diseases.

scholarly journals Modular machine learning for Alzheimer's disease classification from retinal vasculature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianqiao Tian ◽  
Glenn Smith ◽  
Han Guo ◽  
Boya Liu ◽  
Zehua Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Classification Accuracy ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Retinal Vasculature ◽  
Small Vessels ◽  
The Uk ◽  
Saliency Analysis

AbstractAlzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.

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