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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages1076-1080
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
Duration: Jan 14 2007Jan 17 2007

Publication series

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

Conference

Conference7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
CountryUnited States
CityBoston, MA
Period01/14/0701/17/07

Keywords

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

Fingerprint Dive into the research topics of 'Gene expression profiling and machine learning to understand and predict primary graft dysfunction'. Together they form a unique fingerprint.

Cite this