解码阿尔茨海默病的遗传合并症网络。

IF 4 3区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biodata Mining Pub Date : 2024-10-09 DOI:10.1186/s13040-024-00394-w

Xueli Zhang, Dantong Li, Siting Ye, Shunming Liu, Shuo Ma, Min Li, Qiliang Peng, Lianting Hu, Xianwen Shang, Mingguang He, Lei Zhang

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引用次数: 0

摘要

阿尔茨海默病（AD）已成为老年人群中最常见、最复杂的神经退行性疾病。然而，人们对阿尔茨海默病的遗传合并症病因仍知之甚少。在这项研究中，我们对41种AD表型合并症进行了多效性分析，确定了10种遗传合并症与16个与AD相关的多效性基因。通过生物功能和网络分析，我们阐明了AD遗传合并症的分子和功能图谱。此外，利用AD遗传合并症的多效基因和已报道的生物标志物，我们还发现了50种潜在的AD诊断生物标志物。我们的研究结果加深了人们对AD遗传合并症发生的理解，并为寻找AD诊断标志物提供了新的见解。

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Decoding the genetic comorbidity network of Alzheimer's disease.

Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers.

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来源期刊

Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

7.90

自引率

0.00%

发文量

审稿时长

23 weeks

期刊介绍： BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.