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基于数据挖掘的骨骼肌减少症发病机制与运动的关系

The Relationship between the Mechanism of Sarcopenia and Exercise Based on Data Mining.

机构信息

School of Physical Education, Xinxiang Medical University, Xinxiang, 453000 Henan, China.

Department of Physical Education, Gyeongsang National University, Jinju, 52822 Gyeongsangnam-do, Republic of Korea.

出版信息

Comput Math Methods Med. 2022 Jan 22;2022:9339905. doi: 10.1155/2022/9339905. eCollection 2022.

Abstract

Due to the increasing prosperity of human life science and technology, many huge research results have been obtained, and the scientific research of molecular biology is developing rapidly. Therefore, the output of biological genome data has increased exponentially, which constitutes a huge amount of data analysis. The seemingly chaotic and massive amount of data information actually contains a large amount of data and information of great key scientific significance and value. Therefore, this kind of genomic data information not only contains the information content that describes the characteristics of human life but also contains the information content that can express the essence of the biological organism. It includes macroeconomic information that can reflect the basic structure and capabilities of living organisms and microinformation in related fields of molecular biology. This massive amount of genetic data is usually closely related to each other, can influence each other, and does not exist alone. In the article, the causes of uncertain data and the classification of uncertain data are introduced, and the basic concepts and related algorithms of data mining are explained. Focusing on the research and analysis of abnormal point detection and clustering algorithms in uncertain data mining technology, this paper solves the problem of how to obtain more diverse and accurate outlier detection and cluster analysis results in uncertain data. The results showed that whether it was related to obesity or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, the correlation analysis showed that ASM/height was negatively correlated with Lp(a). ASM/height is one of the criteria for diagnosing sarcoidosis, and it is also the core of the analysis. Among the 1956 tumor patients collected in this study, 432 had sarcopenia, accounting for 22.08%, and the incidence of sarcopenia in patients with gastrointestinal tumors increased.

摘要

由于人类生命科学和技术的日益繁荣,取得了许多巨大的研究成果,分子生物学的科学研究也在迅速发展。因此,生物基因组数据的产出呈指数级增长,构成了巨大的数据量分析。看似混乱和庞大的数据量实际上包含了大量具有重大关键科学意义和价值的数据和信息。因此,这种基因组数据信息不仅包含描述人类生命特征的信息内容,还包含可以表达生物机体本质的信息内容。它包括可以反映生物体基本结构和功能的宏观经济信息以及分子生物学相关领域的微观信息。这种海量的遗传数据通常彼此紧密相关、相互影响,而不是单独存在的。文章介绍了不确定数据的产生原因和不确定数据的分类,阐述了数据挖掘的基本概念和相关算法。本文重点研究和分析不确定数据挖掘技术中的异常点检测和聚类算法,解决如何在不确定数据中获得更多样、更准确的异常点检测和聚类分析结果的问题。结果表明,无论是否与肥胖有关,肌少症组的 Lp(a)水平明显高于非肌少症组。同时,相关分析表明,ASM/身高与 Lp(a)呈负相关。ASM/身高是诊断肌少症的标准之一,也是分析的核心。在本研究中收集的 1956 名肿瘤患者中,有 432 名患有肌少症,占 22.08%,胃肠道肿瘤患者的肌少症发病率增加。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/41d8/8800625/dbfd7a29e05e/CMMM2022-9339905.001.jpg

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