ArticleJournal of Alzheimer's disease & Parkinsonism2019
Identification and Analysis of Alzheimer's Candidate Genes by an Amplitude Deviation Algorithm.
Article in Journal of Alzheimer's disease & Parkinsonism, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 24 citations in OpenAlex.
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- Neuroglia Cells Transcriptomic in Brain Development, Aging and Neurodegenerative Diseases.Aging and disease · 2023Review
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- Reconsidering the role of blood-brain barrier in Alzheimer's disease: From delivery to target.Frontiers in aging neuroscience · 2023Review
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- Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource.International journal of molecular sciences · 2022Article
- Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue.Translational psychiatry · 2021Article
- Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis.Carcinogenesis · 2020Article
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundAlzheimer's disease (AD) is the most common form of senile dementia. However, its pathological mechanisms are not fully understood. In order to comprehend AD pathological mechanisms, researchers employed AD-related DNA microarray data and diverse computational algorithms. More efficient computational algorithms are needed to process DNA microarray data for identifying AD-related candidate genes.
methodsIn this paper, we propose a specific algorithm that is based on the following observation: When an acrobat walks along a steel-wire, his/her body must have some swing; if the swing can be controlled, then the acrobat can maintain the body balance. Otherwise, the acrobat will fall. Based on this simple idea, we have designed a simple, yet practical, algorithm termed as the Amplitude Deviation Algorithm (ADA). Deviation, overall deviation, deviation amplitude, and 3δ are introduced to characterize ADA.
results52 candidate genes for AD have been identified via ADA. The implications for some of the AD candidate genes in AD pathogenesis have been discussed.
conclusionsThrough the analysis of these AD candidate genes, we believe that AD pathogenesis may be related to the abnormality of signal transduction (AGTR1 and PTAFR), the decrease in protein transport capacity (COL5A2 (221729_at), COL5A2 (221730_at), COL4A1), the impairment of axon repair (CNR1), and the intracellular calcium dyshomeostasis (CACNB2, CACNA1E). However, their potential implication for AD pathology should be further validated by wet lab experiments as they were only identified by computation using ADA.
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