A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

Huang, Yanting; Sun, Xiaobo; Jiang, Huige; Yu, Shaojun; Robins, Chloe; Armstrong, Matthew; Li, Ronghua; Mei, Zhen; Shi, Xiaochuan; Gerasimov, Ekaterina S.; Jager, Philip L. De; Bennett, David A.; Wingo, Thomas S.; Jin, Peng; Qin, Zhaohui S.

doi:10.1038/s41467-021-24710-8

Cited by 37 publications

(33 citation statements)

References 73 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RBFOX1 (SNP: rs55642412, CorrectedP: 3.18755E-23) has been found to play a role in neuronal development ( Raghavan et al, 2020 ). PTPRD (SNP: rs62538998, CorrectedP: 1.92988E-19) has been confirmed to be related to AD and MCI in previous studies ( Huang et al, 2021 ). DLGAP2 (SNP: rs72507619, CorrectedP: 3.74049E-17) was found to be predominantly expressed in the brain and associated with a wide variety of neurological disorders ( Linthorst et al, 2020 ).…”

Section: Resultssupporting

confidence: 58%

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Meng

Liu

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

show abstract

Section: Resultssupporting

confidence: 58%

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Meng

Liu

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

“…To date, 19 blood-based biomarkers have been chosen for further study towards the diagnosis of Alzheimer’s disease [ 93 ]. Recently, Huang Y et al created Epigenome-Wide Association Studies (EWAS) plus, a computational technique that employs a supervised machine learning strategy, to expand the coverage of multiple EWASs to the entire genome rather than only about 2% of all CpG sites in the genome [ 94 ].…”

Section: Discussionmentioning

confidence: 99%

Epigenetic Biomarkers as Diagnostic Tools for Neurodegenerative Disorders

Martínez-Iglesias

Naidoo

Cacabelos

et al. 2021

IJMS

View full text Add to dashboard Cite

Epigenetics is the study of heritable changes in gene expression that occur without alterations to the DNA sequence, linking the genome to its surroundings. The accumulation of epigenetic alterations over the lifespan may contribute to neurodegeneration. The aim of the present study was to identify epigenetic biomarkers for improving diagnostic efficacy in patients with neurodegenerative diseases. We analyzed global DNA methylation, chromatin remodeling/histone modifications, sirtuin (SIRT) expression and activity, and the expression of several important neurodegeneration-related genes. DNA methylation, SIRT expression and activity and neuregulin 1 (NRG1), microtubule-associated protein tau (MAPT) and brain-derived neurotrophic factor (BDNF) expression were reduced in buffy coat samples from patients with neurodegenerative disorders. Our data suggest that these epigenetic biomarkers may be useful in clinical practical for the diagnosis, surveillance, and prognosis of disease activity in patients with neurodegenerative diseases.

show abstract

“…To the best of our knowledge, no prior study has reported on AD detection based on cfDNA. However, a study using brain epigenetic analysis identified kinases associated with AD [ 48 ]. DNA methylation analysis based on brain tissue [ 49 ] has achieved good predictive accuracy with an AUC of >0.79 [ 50 ] but not an excellent predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Postmortem brain tissue was used to generate CpG methylation data using the Illumina-450 array. These traits were beta-amyloid accumulation, neurofibrillary tangles (NFTs), Braak staging, Consortium to establish a Registry of Alzheimer’s Disease (CERAD) score, global pathology and cognitive trajectory study using epigenetic analysis of brain tissue [ 48 ]. CpG methylation had AUCs = 0.850−0.962 for trait detection.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer’s Disease

Bahado‐Singh

Radhakrishna

Gordevičius³

et al. 2022

Cells

View full text Add to dashboard Cite

Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer’s disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949–0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95–1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.

show abstract

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

Cited by 37 publications

References 73 publications

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Epigenetic Biomarkers as Diagnostic Tools for Neurodegenerative Disorders

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer’s Disease

Contact Info

Product

Resources

About