Detection of Novel Biomarker Genes of Alzheimer’s Disease Using Gene Expression Data

Perera, S. P. C.; Hewage, Kaveesha; Gunarathne, Chamara; Navarathna, Rajitha; Herath, Damayanthi; Ragel, Roshan

doi:10.1109/mercon50084.2020.9185336

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…Support Vector Machines, Random Forest, Nave Bayes, and (MLP-NN) are four supervised ML approaches that are used to categorize two different groups of samples. One of these models, the RSA-MLP-NN, proved to be extremely effective at differentiating between genes associated with Alzheimer's disease and 2020) [15]. Presented a classification system for predicting AD from the dataset (GEO: GSE63060 and GSE63061), which is referred to it as the AD dataset.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Model for Prediction Alzheimer's disease Based on Microarray Gene Expression Data

kareem

2022

Jour. Kufa Math. Comp.

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Alzheimer's disease (AD) is a major productive neurological illness with complicated genetic architecture. One of the main aims of biomedical research is to identify risk genes and then explain how these genes contribute to disease development. As a result, it is required to increase the list of genes linked to Alzheimer's disease. Genes play a crucial role in every biological activity. Microarray technology has given genes access to a large number of genes, allowing them to evaluate several levels of expression at the same time. Microarray datasets are categorized by a huge number of genes and small sample sizes. This reality is referred to as a multidimensional curse with a difficult task. A promising technology known as gene selection is addressing this issue and has the potential to revolutionize Alzheimer's disease diagnosis. In this work, gene selection approaches such as Singular Value Decomposition (SVD) and Principle Component Analysis (PCA) were used. Techniques can help to minimize an amount from trivial and redundant gene in the unique datasets. Then, using the Convolutional Neural Network (CNN) as a classifier, deep learning (DL) is used to predict AD. The dataset was processed using a CNN with seven layers and varied settings. With the AD dataset, the empirical findings reveal that the PCA-CNN model has 96.60 percent accuracy and a loss of 0.3503, while the SVD-CNN model has 97.08% accuracy and a loss of 0.2466. As a result, the suggested approach is suited for reducing gene dimensions and improving classification accuracy by choosing a subset of relevant genes.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Model for Prediction Alzheimer's disease Based on Microarray Gene Expression Data

kareem

2022

Jour. Kufa Math. Comp.

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“…It is a powerful tool that can monitor the expression of thousands of genes at the same time and profile valuable information about the gene expression process. Gene expression profiles can help understand the basic genetic structure of a disease through discovering genes involved in its formation [46]. They have the ability to visualize the physiological changes of an AD patient and guide many researchers to understand the biological aspects of the disease pathology [47].…”

Section: Genomics Biomarkersmentioning

confidence: 99%

“…In [46] gene expression data were exploited to classify AD and discover new genomics biomarkers associated with AD. The researchers at first ranked expressed genes with P-value by using T-test in order to remove genes with P-value less than 0.5 as they have significantly different expressed values than the two sample classes, AD, and NC.…”

Section: Gene Expression Profiles Datamentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It can cause a massive impact on a patient's memory and mobility. As this disease is irreversible, early diagnosis is crucial for delaying the symptoms and adjusting the patient's lifestyle. Many machine learning (ML) and deep learning (DL) based-approaches have been proposed to accurately predict AD before its symptoms onset. However, finding the most effective approach for AD early prediction is still challenging. This review explored 24 papers published from 2018 until 2021. These papers have proposed different approaches using state of the art machine learning and deep learning algorithms on different biomarkers to early detect AD. The review explored them from different perspectives to derive potential research gaps and draw conclusions and recommendations. It classified these recent approaches in terms of the learning technique used and AD biomarkers. It summarized and compared their findings, and defined their strengths and limitations. It also provided a summary of the common AD biomarkers. From this review, it was found that some approaches strove to increase the prediction accuracy regardless of their complexity such as using heterogeneous datasets, while others sought to find the most practical and affordable ways to predict the disease and yet achieve good accuracy such as using audio data. It was also noticed that DL based-approaches with image biomarkers remarkably surpassed ML based-approaches. However, they achieved poorly with genetic variants data. Despite the great importance of genetic variants biomarkers, their large variance and complexity could lead to a complex approach or poor accuracy. These data are crucial to discover the underlying structure of AD and detect it at early stages. However, an effective pre-processing approach is still needed to refine these data and employ them efficiently using the powerful DL algorithms.

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“…Cross-validation across different cohorts has also been employed by plasma proteomics researchers to solve the over-fitting issue in high dimensional investigations. It has been proposed it due to AD being a disease associated with protected system malfunction and mitochondrial dysfunction [12,13], concentrating on genes implicated in pertinent pathways could aid in the finding of biomarkers [14 ,15]. Only a small number of earlier investigations, though, have modeled using biological data.…”

Section: Introductionmentioning

confidence: 99%

Identification of Alzheimer’s Disease Hub Genes Based on Improved HITS Algorithm

Kadhim,

Abd ul kader,

Altameemi

et al. 2024

Jour. Kufa Math. Comp.

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Alzheimer's disease is a severe, neurodegenerative condition that gradually breaks memories, thinking abilities, and the ability to carry out even the most basic tasks. The hub genes of AD were examined in this study. They understand how interactions between proteins and non-protein substances are crucial to understanding how proteins work. Network investigations of protein-protein interactions, in particular, help understand biological issues. This article offers a novel approach to identifying essential proteins using weighted PPI networks and Hyperlink-Induced Topic Search (HITS) algorithm. We discovered the top 10 hub genes linked to AD using a protein network analysis: AKT1, TGFB1, GRB2, NFKB1, PIK3CA, PIK3R1, TNF, IFNG, VEGFA, and TP53. It was discovered by gene enrichment that most gene activities might be categorized as vital to the plasma membrane, including engagement in signaling cascades, G-protein composite reliability activation, and cell contact. The prioritized genes were determined by the convergent functional genomics ranking AKT1, TGFB1, GRB2, NFKB1, PIK3CA, PIK3R1, TNF, IFNG, VEGFA, and TP53. To better understand AD pathophysiology and find new biomarkers or medication targets for AD treatment, these molecular pathways hub genes will be helpful.

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Detection of Novel Biomarker Genes of Alzheimer’s Disease Using Gene Expression Data

Cited by 11 publications

References 20 publications

A Deep Learning Model for Prediction Alzheimer's disease Based on Microarray Gene Expression Data

A Deep Learning Model for Prediction Alzheimer's disease Based on Microarray Gene Expression Data

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Identification of Alzheimer’s Disease Hub Genes Based on Improved HITS Algorithm

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