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Prediction the Age of Human Brains from Gene Expression

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Cognitive Systems and Signal Processing (ICCSIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1397))

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Abstract

Understanding temporal characteristics of gene expression in normal human brain can help explain the neurodevelopment, working mechanism and functional diversity. Based on the gene expression dataset of developing human brains from the Allen Brain Atlas, we accurately predicted the age of human brains using support vector machine and identified 9,934 age related genes. Significant changes occur in gene expression of human brains before and after birth, thus we establish support vector machine (SVM) models for the subjects before birth and after birth, respectively. In general, the age of subjects can be well predicted by the SVM models, with the Pearson correlation coefficient of predicted age and the labeled age of all subjects is 0.9397 with P-value < 0.001 (before birth: r = 0.9465, P-value < 0.001; after birth: r = 0.9121, P-value < 0.001). For the total subjects, mean absolute error (MAE) of age prediction is 2.82 years with standard error (SE) is 0.15 years (before birth: MAE = 1.03 post-conceptual weeks (pcws), SE = 0.08 pcws; after birth: MAE = 4.70 years, SE = 0.20 years). This investigation reveal the bulk of temporal regulation occurred during prenatal development. By analyzing the functional annotations of age related genes, we found expression differences of genes before and after birth may be related to their functions. Finally, we found the prediction accuracy of each period can reflect its specificity of gene expression, which is negatively correlated to the gene expression similarity across periods. This study provides new insights into temporal dynamic pattern of gene expression in human brains and its relationship with functions.

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Acknowledgements

This study was supported by the National Science Foundation of China (61722313, 61503397, 61420106001, and 61773391) and the Fok Ying Tung Education Foundation (161057).

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Authors and Affiliations

  1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, People’s Republic of China

    Wei Liu, Jian Qin, Lingli Zeng, Hui Shen & Dewen Hu

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  1. Wei Liu
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  2. Jian Qin
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  3. Lingli Zeng
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  4. Hui Shen
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  5. Dewen Hu
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Corresponding author

Correspondence to Dewen Hu .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Tsinghua University, Beijing, China

    Huaping Liu

  3. Tsinghua University, Beijing, China

    Bin Fang

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Liu, W., Qin, J., Zeng, L., Shen, H., Hu, D. (2021). Prediction the Age of Human Brains from Gene Expression. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_31

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  • DOI: https://doi.org/10.1007/978-981-16-2336-3_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2335-6

  • Online ISBN: 978-981-16-2336-3

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