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阿尔茨海默病:利用基因/蛋白质网络机器学习发现橄榄油中的分子。

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil.

机构信息

Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.

Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA.

出版信息

Hum Genomics. 2023 Jul 7;17(1):57. doi: 10.1186/s40246-023-00503-6.

DOI:10.1186/s40246-023-00503-6
PMID:37420280
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10327379/
Abstract

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

摘要

阿尔茨海默病(AD)给人类、社会和经济带来了沉重的负担。先前的研究表明,特级初榨橄榄油(EVOO)可能有助于预防认知能力下降。在这里,我们提出了一种网络机器学习方法,用于识别 EVOO 中具有最高潜力影响与 AD 发展和进展相关的蛋白质网络的生物活性植物化学物质。在五重交叉验证设置中,针对 AD 的晚期实验药物对其他临床批准药物的预测达到了 70.3±2.6%的平衡分类准确性。然后,使用经过校准的机器学习算法来预测现有药物和已知 EVOO 植物化学物质与影响 AD 蛋白质网络的药物具有相似作用的可能性。这些分析确定了以下十种最有可能针对 AD 发挥作用的 EVOO 植物化学物质:槲皮素、染料木黄酮、木樨草素、棕榈油酸、硬脂酸、芹菜素、表儿茶素、山奈酚、角鲨烯和大豆苷元(按从最高到最低的可能性排列)。这项计算机研究提出了一个框架,将人工智能、分析化学和组学研究结合在一起,以识别独特的治疗剂。它为 EVOO 成分如何帮助治疗或预防 AD 提供了新的见解,并可能为未来的临床研究提供考虑的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14f7/10327379/b40de20aa38b/40246_2023_503_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14f7/10327379/16424a631913/40246_2023_503_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14f7/10327379/b40de20aa38b/40246_2023_503_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14f7/10327379/16424a631913/40246_2023_503_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14f7/10327379/b40de20aa38b/40246_2023_503_Fig2_HTML.jpg

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