A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease

Mahendran, N.; Vincent, Durai Raj

doi:10.1016/j.compbiomed.2021.105056

Cited by 66 publications

(35 citation statements)

References 32 publications

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“…Several other diseases, including ulcerative colitis, heart failure, and polycystic ovary syndrome, have already benefited from this innovative research technique (Li et al, 2020 ; Tian et al, 2020 ; Xie et al, 2020 ). Prior to this, a few AD prediction models based on methylated gene biomarkers had been developed (Ren et al, 2020 ; Mahendran and PM, 2022 ). However, some problems exist in these studies, such as small sample size or general prediction effect of the established models.…”

Section: Discussionmentioning

confidence: 99%

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Sun

Peng

2022

Front. Aging Neurosci.

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Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.

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Section: Discussionmentioning

confidence: 99%

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Sun

Peng

2022

Front. Aging Neurosci.

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“…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. Another study with the same dataset [ 49 ] along with an additional dataset [ 51 ] also did not reach an excellent predictive accuracy [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…The AUC for the detection of NFT, a hallmark of AD, was equal to 0.962. A study of DNA methylation in brain tissue [ 49 ] reported good predictive accuracy, with an AUC of >0.79 for AD detection [ 50 ]. A subsequent in silico publication combining two prior datasets [ 49 ] in which methylation profiling of the superior temporal gyrus of AD cases and controls was used [ 51 ].…”

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

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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.

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“…In this vein, a complete understanding of its biomarkers is essential to differentiate AD symptoms from normal aging symptoms and accordingly slow its progression. Indeed, many neurological disorders directly impact the brain, particularly the hippocampus, which is essential in forming memories [12], emotional control, and learning. Hippocampus damage has been linked to various neurological and psychiatric disorders, including AD [12].…”

Section: Introductionmentioning

confidence: 99%

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Baghdadi

Malki

Balaha

et al. 2022

Sensors

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Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.

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A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease

Cited by 66 publications

References 32 publications

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

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

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

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