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Integrative Classification Using Structural Equation Modeling of Homeostasis

Author

Listed:
  • Hong-Bin Fang

    (Georgetown University Medical Center)

  • Hengzhen Huang

    (Guangxi Normal University)

  • Ao Yuan

    (Georgetown University Medical Center)

  • Ruzong Fan

    (Georgetown University Medical Center)

  • Ming T. Tan

    (Georgetown University Medical Center)

Abstract

We consider binary classification in the high-dimensional setting, where the number of features is huge, and the number of observations is limited. We focus on the setting where features in one group have certain correlation structures that are not present in the other group. This is particularly relevant in early detection of diseases where subjects develop from a normal or homeostatic state to a diseased condition. Linear discriminant analysis (with a link function) and classification based on regularized regression or machine learning have been used as methods for this problem and related variable selection. However, most methods do not account for the correlation structures of variables within groups. While the diseased group may demonstrate abundant diversity and no clear structure, achieving higher accuracy in classification requires considering the correlation structures in the control group with homeostasis. In this paper, we develop a structural equation modeling approach to characterize the correlation structures of homeostasis, and the parameters are estimated using only the data from one group. The structural equation models are not applicable to the data from the other group, and the classification specificity and sensitivity are determined by choosing the confidence intervals of the estimated parameters. We use a real multi-platform genomics dataset to illustrate the methods, and we demonstrate that our approach performs well compared to statistical learning methods such as regularized logistic regression models.

Suggested Citation

  • Hong-Bin Fang & Hengzhen Huang & Ao Yuan & Ruzong Fan & Ming T. Tan, 2024. "Integrative Classification Using Structural Equation Modeling of Homeostasis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(3), pages 742-760, December.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:3:d:10.1007_s12561-024-09418-9
    DOI: 10.1007/s12561-024-09418-9
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    References listed on IDEAS

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    1. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2014. "Probability-enhanced sufficient dimension reduction for binary classification," Biometrics, The International Biometric Society, vol. 70(3), pages 546-555, September.
    2. Liu Zhenqiu & Jiang Feng & Tian Guoliang & Wang Suna & Sato Fumiaki & Meltzer Stephen J. & Tan Ming, 2007. "Sparse Logistic Regression with Lp Penalty for Biomarker Identification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-22, February.
    3. Chen, Song Xi & Zhang, Li-Xin & Zhong, Ping-Shou, 2010. "Tests for High-Dimensional Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 810-819.
    4. Yuan Huang & Qingzhao Zhang & Sanguo Zhang & Jian Huang & Shuangge Ma, 2017. "Promoting Similarity of Sparsity Structures in Integrative Analysis With Penalization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 342-350, January.
    5. Jianqing Fan & Yang Feng & Xin Tong, 2012. "A road to classification in high dimensional space: the regularized optimal affine discriminant," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 745-771, September.
    6. Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
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