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References from Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data. Local targets link to admitted publications; unresolved targets remain external evidence.
Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data
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Selective preservation and degeneration within the prefrontal cortex in aging and Alzheimer disease
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Alzheimer disease, attention, and the cholinergic system
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10.1016/j.bcp.2013.12.024
10.1016/j.bcp.2013.12.024 · External reference
A machine learning method for identifying critical interactions between gene pairs in Alzheimer's disease prediction
10.3389/fneur.2019.01162 · 2019 · External reference
Identification of methylated gene biomarkers in patients with Alzheimer's disease based on machine learning
2020 · External reference
Detecting diagnostic biomarkers of Alzheimer's disease by integrating gene expression data in six brain regions
10.3389/fgene.2019.00157 · 2019 · External reference
A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap
10.1093/brain/awz384 · 2020 · External reference
Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
2018 · External reference
Neural network-based multiomics data integration in Alzheimer's disease
10.1145/3319619.3321920 · 2019 · External reference
Breast cancer prognostics using multi-omics data
2016 · External reference
Machine learning and systems genomics approaches for multi-omics data
10.1186/s40364-017-0082-y · 2017 · External reference
10.1007/s40484-016-0063-4
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Discovery of bladder Cancer-related genes using integrative heterogeneous network modeling of multi-omics data
10.1038/s41598-017-15890-9 · 2017 · External reference
Integration of multi-omics data for gene regulatory network inference and application to breast cancer
10.1109/tcbb.2018.2866836 · 2018 · External reference
Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset
10.1016/j.eswa.2019.112873 · 2020 · External reference
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Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction
10.1016/j.cmpb.2020.105765 · 2020 · External reference
A hybrid data mining model for diagnosis of patients with clinical suspicion of Dementia
10.1016/j.cmpb.2018.08.016 · 2018 · External reference
Super-resolution of magnetic resonance images using deep convolutional neural networks
10.1109/icce-china.2017.7990985 · 2017 · External reference
Epileptic seizure prediction over EEG data using hybrid CNN-SVM model with edge computing services
10.1051/matecconf/201821003016 · 2018 · External reference
Emotion AI-driven sentiment analysis: A survey, future research directions, and open issues
10.3390/app9245462 · 2019 · External reference
Multi-class skin lesion detection and classification via teledermatology
10.1109/jbhi.2021.3067789 · 2021 · External reference
Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification
10.1016/j.compeleceng.2020.106956 · 2021 · External reference
Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks
2016 · External reference
Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data
10.1109/ftc.2016.7821697 · 2016 · External reference
A deep CNN based multi-class classification of Alzheimer's disease using MRI
2017 · External reference
Early diagnosis of Alzheimer's disease using deep learning
2019 · External reference
A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks
10.1007/s10916-019-1475-2 · 2020 · External reference
Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning
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Elevated DNA methylation across a 48-kb region spanning the HOXA gene cluster is associated with Alzheimer's disease neuropathology
10.1016/j.jalz.2018.01.017 · 2018 · External reference
Analysis of microarray data using Z score transformation
10.1016/s1525-1578(10)60455-2 · 2003 · External reference
Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis
10.1186/1471-2105-11-587 · 2010 · External reference
Using machine learning to design and interpret gene-expression microarrays
2004 · External reference
Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification
10.1186/s13148-020-00842-4 · 2020 · External reference
10.2196/preprints.25102
10.2196/preprints.25102 · External reference
Restricted Boltzmann Machines and Deep Belief Networks on multi-core processors
2012 · External reference
10.1162/neco.2008.04-07-510
10.1162/neco.2008.04-07-510 · External reference
