Gene expression profiling and machine learning to understand and predict primary graft dysfunction

Monika Ray; Sekhar Dharmarajan; Johannes Freudenberg; G. Alexander Patterson; Weixiong Zhang

doi:10.1109/BIBE.2007.4375692

Gene expression profiling and machine learning to understand and predict primary graft dysfunction

Monika Ray, Sekhar Dharmarajan, Johannes Freudenberg, G. Alexander Patterson, Weixiong Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Lung transplantation is the treatment of choice for end-stage pulmonary diseases. A limited donor supply has resulted in 4000 patients on the waiting list. Currently, 10-20% of donor organs are deemed suitable under the selection criteria, of which 15-25% fails due to primary graft dysfunction (PGD). In this study, we attempt to further our understanding of PGD by observing the changes in gene expression across donor lungs that developed PGD versus those that did not. Our second goal is to use a machine learning tool - support vector machine (SVM), to distinguish unsuitable donor lungs from suitable donor lungs, based on the gene expression data. Classification results for distinguishing suitable and unsuitable lungs for transplantation using a SVM were promising. This is the first such attempt to use human lungs used for transplantation and combine the identification of a molecular signature for PGD, with machine learning methods for donor lung prediction.

Original language	English
Title of host publication	Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages	1076-1080
Number of pages	5
DOIs	https://doi.org/10.1109/BIBE.2007.4375692
State	Published - 2007
Event	7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States Duration: Jan 14 2007 → Jan 17 2007

Publication series

Name	Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Conference

Conference	7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Country/Territory	United States
City	Boston, MA
Period	01/14/07 → 01/17/07

Keywords

Donor lungs evaluation
Gene network analysis
Lung transplantation
Primary graft dysfunction
SVM classification

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10.1109/BIBE.2007.4375692

Cite this

Ray, M., Dharmarajan, S., Freudenberg, J., Patterson, G. A., & Zhang, W. (2007). Gene expression profiling and machine learning to understand and predict primary graft dysfunction. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE (pp. 1076-1080). Article 4375692 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). https://doi.org/10.1109/BIBE.2007.4375692

Ray, Monika ; Dharmarajan, Sekhar ; Freudenberg, Johannes et al. / Gene expression profiling and machine learning to understand and predict primary graft dysfunction. Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. pp. 1076-1080 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE).

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abstract = "Lung transplantation is the treatment of choice for end-stage pulmonary diseases. A limited donor supply has resulted in 4000 patients on the waiting list. Currently, 10-20% of donor organs are deemed suitable under the selection criteria, of which 15-25% fails due to primary graft dysfunction (PGD). In this study, we attempt to further our understanding of PGD by observing the changes in gene expression across donor lungs that developed PGD versus those that did not. Our second goal is to use a machine learning tool - support vector machine (SVM), to distinguish unsuitable donor lungs from suitable donor lungs, based on the gene expression data. Classification results for distinguishing suitable and unsuitable lungs for transplantation using a SVM were promising. This is the first such attempt to use human lungs used for transplantation and combine the identification of a molecular signature for PGD, with machine learning methods for donor lung prediction.",

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author = "Monika Ray and Sekhar Dharmarajan and Johannes Freudenberg and Patterson, {G. Alexander} and Weixiong Zhang",

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Ray, M, Dharmarajan, S, Freudenberg, J, Patterson, GA & Zhang, W 2007, Gene expression profiling and machine learning to understand and predict primary graft dysfunction. in Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE., 4375692, Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, pp. 1076-1080, 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, Boston, MA, United States, 01/14/07. https://doi.org/10.1109/BIBE.2007.4375692

Gene expression profiling and machine learning to understand and predict primary graft dysfunction. / Ray, Monika; Dharmarajan, Sekhar; Freudenberg, Johannes et al.
Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. p. 1076-1080 4375692 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Ray M, Dharmarajan S, Freudenberg J, Patterson GA, Zhang W. Gene expression profiling and machine learning to understand and predict primary graft dysfunction. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. p. 1076-1080. 4375692. (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). doi: 10.1109/BIBE.2007.4375692

Gene expression profiling and machine learning to understand and predict primary graft dysfunction

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