A polynomial based model for cell fate prediction in human diseases
Background: Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. Results:...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/86071 http://hdl.handle.net/10220/45263 |
| _version_ | 1811676454155452416 |
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| author | Ma, Lichun Zheng, Jie |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Ma, Lichun Zheng, Jie |
| author_sort | Ma, Lichun |
| collection | NTU |
| description | Background: Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. Results: In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Conclusions: Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases. |
| first_indexed | 2024-10-01T02:21:43Z |
| format | Journal Article |
| id | ntu-10356/86071 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:21:43Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/860712020-03-07T11:48:52Z A polynomial based model for cell fate prediction in human diseases Ma, Lichun Zheng, Jie School of Computer Science and Engineering Biomedical Informatics Lab Complexity Institute Cell Fate Prediction Cell Death Background: Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. Results: In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Conclusions: Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases. MOE (Min. of Education, S’pore) Published version 2018-07-26T08:17:46Z 2019-12-06T16:15:27Z 2018-07-26T08:17:46Z 2019-12-06T16:15:27Z 2017 Journal Article Ma, L., & Zheng, J. (2017). A polynomial based model for cell fate prediction in human diseases. BMC Systems Biology, 11(S7), 126-. https://hdl.handle.net/10356/86071 http://hdl.handle.net/10220/45263 10.1186/s12918-017-0502-5 en BMC Systems Biology © 2017 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 13 p. application/pdf |
| spellingShingle | Cell Fate Prediction Cell Death Ma, Lichun Zheng, Jie A polynomial based model for cell fate prediction in human diseases |
| title | A polynomial based model for cell fate prediction in human diseases |
| title_full | A polynomial based model for cell fate prediction in human diseases |
| title_fullStr | A polynomial based model for cell fate prediction in human diseases |
| title_full_unstemmed | A polynomial based model for cell fate prediction in human diseases |
| title_short | A polynomial based model for cell fate prediction in human diseases |
| title_sort | polynomial based model for cell fate prediction in human diseases |
| topic | Cell Fate Prediction Cell Death |
| url | https://hdl.handle.net/10356/86071 http://hdl.handle.net/10220/45263 |
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