Volume 8, Issue 3 (12-2021)
jhbmi 2021, 8(3): 270-281 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Eskandarian P, Bagherzadeh Mohasefi J, Pirnejad H, Niazkhani Z. Multivariate Feature Extraction for Prediction of Future Gene Expression Profile. jhbmi 2021; 8 (3) :270-281
URL: http://jhbmi.ir/article-1-610-en.html
URL: http://jhbmi.ir/article-1-610-en.html
Ph.D. in Software Engineering, Associate Professor, Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran
Abstract: (6367 Views)
Introduction: The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of hematopoietic stem cells.
Method: The developed neural network takes the parent hematopoietic stem cell’s gene expression profile as input and generates the gene expression profiles of its future descendant cells. A temporal attention was proposed to encode the main time series and a spatial attention was also provided to encode the secondary time series.
Results: To make an acceptable prediction, the gene expression profiles of at least four initial division/differentiation steps must be known. The designed neural network surpasses the existing neural networks in terms of prediction accuracy and number of correctly predicted division/differentiation steps. The proposed scheme can predict hundreds of division/differentiation steps. The proposed scheme’ error in prediction of 1, 4, 16, 64, and 128 division/differentiation steps was 3.04, 3.76, 5.5, 7.83, and 11.06 percent, respectively.
Conclusion: Based on the gene expression profile of a parent hematopoietic stem cell, the gene expression profiles of its descendants can be predicted for hundreds of division/differentiation steps and if necessary, solutions must be sought to encounter future genetic disorders.
Method: The developed neural network takes the parent hematopoietic stem cell’s gene expression profile as input and generates the gene expression profiles of its future descendant cells. A temporal attention was proposed to encode the main time series and a spatial attention was also provided to encode the secondary time series.
Results: To make an acceptable prediction, the gene expression profiles of at least four initial division/differentiation steps must be known. The designed neural network surpasses the existing neural networks in terms of prediction accuracy and number of correctly predicted division/differentiation steps. The proposed scheme can predict hundreds of division/differentiation steps. The proposed scheme’ error in prediction of 1, 4, 16, 64, and 128 division/differentiation steps was 3.04, 3.76, 5.5, 7.83, and 11.06 percent, respectively.
Conclusion: Based on the gene expression profile of a parent hematopoietic stem cell, the gene expression profiles of its descendants can be predicted for hundreds of division/differentiation steps and if necessary, solutions must be sought to encounter future genetic disorders.
Keywords: Hematopoietic Stem Cell, Neural Network, Multivariate Time Series, Gene Expression Profile, Prediction
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2021/07/6 | Accepted: 2021/12/15
Received: 2021/07/6 | Accepted: 2021/12/15
Audio File [MP3 1015 KB] (40 Download)
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
