title: ÐÐ½Ð°Ð»Ð¸Ð· Ð³ÐµÐ½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÑÑÑÐºÑÑÑÐ½ÑÐ¼Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸ÑÐ¼Ð¸ (Ð¿Ð°ÐºÐµÑ SEMgraph)
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Analysis of gene networkswithin of structural equations modeling (SEMgraph package)
Creators: name: ÐÑÐºÑÑÐ½Ð¾Ð²Ð°, ÐÐ²Ð³ÐµÐ½Ð¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÑÐºÑÑÑÐ½ÑÐ¼Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸ÑÐ¼Ð¸
Ð±Ð¾ÐºÐ¾Ð²Ð¾Ð¹ Ð°Ð¼Ð¸Ð¾ÑÑÐ¾ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ ÑÐºÐ»ÐµÑÐ¾Ð·
structural equation models
amyotrophic lateral sclerosis

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description: One of the first places in biomedical research is the discovery and understanding of the mechanisms underlying complex phenotypic traits. The advent of high-throughput DNA and RNA nucleotide sequencing techniques has revealed the true complexity underlying disease features. However, the huge number of publicly available biomedical databases makes it easy to transform structured biochemical and biomedical information into networks and statistical models. The purpose of studying gene network modeling methods using the R SEMgraph package is to validate experimental data for various diseases and evaluate the sensitivity of the results to variations in various parameters. The package allows the user to select initial settings and evaluate algorithms and model parameters directly from quantitative data in an efficient and parallelizable manner and infer causal relationships in complex biological networks. As a practical example, the application of the methods from the package to amyotrophic lateral sclerosis from the Kyoto encyclopedia of genes and genomes was described. Using causal structure learning algorithms, it was possible to extract an acyclic graph model with good fitting results. Additional numerical and graphical results of the network survey were also presented. After evaluating the change in parameters, it was found that the standard parameters of decreasing graph density, increasing latent covariance, and increasing the maximum allowable geodesic distance for directional and non-directional shortest path search are optimal for amyotrophic sclerosis data.