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  • Open Access

    ARTICLE

    Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

    Aliaa El-Gawady1,*, BenBella S. Tawfik1, Mohamed A. Makhlouf1,2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5559-5572, 2023, DOI:10.32604/cmc.2023.034734 - 28 December 2022

    Abstract Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer’s disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are… More >

  • Open Access

    ARTICLE

    Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

    Meshal Alharbi, Shabana R. Ziyad*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5483-5505, 2023, DOI:10.32604/cmc.2023.032341 - 28 December 2022

    Abstract Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature… More >

  • Open Access

    REVIEW

    Presenilin and Alzheimer’s disease interactions with aging, exercise and high-fat diet: A systematic review

    YINGHUI GAO, DENGTAI WEN*, SHIJIE WANG, JINGFENG WANG

    BIOCELL, Vol.47, No.1, pp. 41-49, 2023, DOI:10.32604/biocell.2022.022689 - 26 September 2022

    Abstract Presenilin (Psn) protein is associated with organismal aging. Mutations in the Psn gene may lead to Alzheimer’s disease (AD), dilated cardiomyopathy (DCM), and many age-dependent degenerative diseases. These diseases seriously affect the quality of life and longevity of the population and place a huge burden on health care and economic systems around the world. Humans have two types of Psn, presenilin-1 (PSEN1) and presenilin-2 (PSEN2). Mutations in the genes encoding PSEN1, PSEN2, and amyloid precursor protein (APP) have been identified as the major genetic causes of AD. Psn is a complex gene strongly influenced by genetic and… More >

  • Open Access

    ARTICLE

    Prediction of Alzheimer’s Using Random Forest with Radiomic Features

    Anuj Singh*, Raman Kumar, Arvind Kumar Tiwari

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 513-530, 2023, DOI:10.32604/csse.2023.029608 - 16 August 2022

    Abstract Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely… More >

  • Open Access

    ARTICLE

    Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

    S. Meenakshi Ammal*, P. S. Manoharan

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 367-390, 2023, DOI:10.32604/csse.2023.025230 - 01 June 2022

    Abstract Worldwide, many elders are suffering from Alzheimer’s disease (AD). The elders with AD exhibit various abnormalities in their activities, such as sleep disturbances, wandering aimlessly, forgetting activities, etc., which are the strong signs and symptoms of AD progression. Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage. The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients (ADP) using wearables. In the proposed work, a publicly available dataset collected using wearables is applied. Currently,… More >

  • Open Access

    ARTICLE

    Comprehensive analysis of the expression and prognosis for APOE in malignancies: A pan-cancer analysis

    SHOUKAI YU1,2,3,*, LINGMEI QIAN1, JUN MA1,*

    Oncology Research, Vol.30, No.1, pp. 13-22, 2022, DOI:10.32604/or.2022.026141 - 06 December 2022

    Abstract Apolipoprotein E (APOE), a gene identified as one of the strongest genetic factors contributing to the risk determinant of developing late-onset Alzheimer’s disease (AD), may also contribute to the risk of cancer. However, no pan-cancer analysis has been conducted specifically for the APOE gene. In this study, we investigated the oncogenic role of the APOE gene across cancers by GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas). Based on the available data, we found that most cancer types overexpress APOE, and clear associations exist between the expression level of APOE and prognosis in tumor patients. The… More >

  • Open Access

    ARTICLE

    Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

    Atif Mehmood1,*, Ahed Abugabah1, Ahmed Ali AlZubi2, Louis Sanzogni3

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 305-315, 2022, DOI:10.32604/csse.2022.018520 - 23 March 2022

    Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of… More >

  • Open Access

    REVIEW

    Mesenchymal stem cells: As a multi-target cell therapy for clearing β-amyloid deposition in Alzheimer’s disease

    RUXIN ZHANG1, CHENGGANG LI2, RUOCHEN DU1, YITONG YUAN1, BICHUN ZHAO1, YUJUAN ZHANG1, CHUNFANG WANG1,*

    BIOCELL, Vol.46, No.3, pp. 583-592, 2022, DOI:10.32604/biocell.2022.017248 - 18 November 2021

    Abstract Extracellular β-amyloid (Aβ) plaques and neurofibrillary tangles (NFTs) are the pathological hallmarks of Alzheimer’s disease (AD). Studies have shown that aggregates of extracellular Aβ can induce neuroinflammation mediated neurotoxic signaling through microglial activation and release of pro-inflammatory factors. Thus, modulation of Aβ might be a potential therapeutic strategy for modifying disease progression. Recently, a large number of reports have confirmed the beneficial effects of mesenchymal stem cells (MSCs) on AD. It is believed to reduce neuroinflammation, reduce Aβ amyloid deposits and NFTs, increase acetylcholine levels, promote neurogenesis, reduce neuronal damage, and improve working memory and More >

  • Open Access

    ARTICLE

    Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

    Himanshu Padole*, S. D. Joshi, Tapan K. Gandhi

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1655-1669, 2022, DOI:10.32604/iasc.2022.021310 - 09 October 2021

    Abstract Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization More >

  • Open Access

    ARTICLE

    Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

    Nora Shoaip1, Amira Rezk1, Shaker EL-Sappagh2,3, Tamer Abuhmed4,*, Sherif Barakat1, Mohammed Elmogy5

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3531-3548, 2021, DOI:10.32604/cmc.2021.019069 - 24 August 2021

    Abstract Alzheimer’s disease (AD) is a very complex disease that causes brain failure, then eventually, dementia ensues. It is a global health problem. 99% of clinical trials have failed to limit the progression of this disease. The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms. Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction. In this regard, the need becomes more urgent for biomarker-based detection. A key issue in understanding AD is the need… More >

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