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Optimization of Computations for Structural Equation Modeling with Applications in Bionformatics

  • MOLECULAR BIOPHYSICS
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Abstract

Structural equation modeling (SEM) is a technique for analysis of linear relations represented as the causal and correlational relationships between observed and latent variables. SEM is a popular tool in a wide range of fields, from the humanities to the natural sciences. Over the past decade, this method has become especially interesting in areas that are at the interface with biology. However, the common assumption that observations are independent is often violated in biological data, which should be taken into account when constructing a mathematical model. In addition, in genome-wide association studies, the time of optimization of model parameters is a critical factor. In this paper, we propose a new SEM model, as well as a fast way to estimate its parameters.

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Funding

This study was supported by the Russian Foundation for Basic Research (grant no. 18-29-13033).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    G. A. Meshcheryakov, V. A. Zuev, A. A. Igolkina & M. G. Samsonova

  2. Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow oblast, Russia

    G. A. Meshcheryakov

Authors
  1. G. A. Meshcheryakov
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  2. V. A. Zuev
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  3. A. A. Igolkina
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  4. M. G. Samsonova
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Corresponding author

Correspondence to M. G. Samsonova.

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Conflict of interest. The authors declare that they have no conflicts of interest.

Statement of the welfare of animals. The article does not contain any studies involving animals in experiments performed by any of the authors.

Additional information

Translated by M. Batrukova

Abbreviations: SEM, structural equation modelling; GWAS, genome-wide association studies; LMM, linear mixed model; GP, Gaussian process.

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Meshcheryakov, G.A., Zuev, V.A., Igolkina, A.A. et al. Optimization of Computations for Structural Equation Modeling with Applications in Bionformatics. BIOPHYSICS 67, 353–355 (2022). https://doi.org/10.1134/S0006350922030149

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  • DOI: https://doi.org/10.1134/S0006350922030149

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