Combining Gene Expression and Interaction Network Data to Improve Kidney Lesion Score Prediction
- Davoud Moulavi
- Mohsen Hajiloo, Dept of Computing Science
- Joerg Sander, Dept of Computing Science
- Philip Halloran
- Russ Greiner, Dept of Computing Science; PI of AICML
Current method of diagnosing kidney rejection based on histopathology of renal biopsies in form of lesion scores is error-prone. Researchers use gene expression microarrays in combination of machine learning to build better kidney rejection predictors. However the high dimensionality of data makes this task challenging and compels application of feature selection methods. We present a method for predicting lesions using combination of statistical and biological feature selection methods along with an ensemble learning technique. Results show that combining highly interacting genes (Hub Genes) from protein-protein interaction network with genes selected by squared t-test method brings the most accurate kidney lesion score predictor.
Citation
D. Moulavi, M. Hajiloo, J. Sander, P. Halloran, R. Greiner. "Combining Gene Expression and Interaction Network Data to Improve Kidney Lesion Score Prediction". International Journal of Bioinformatics Research and Applications, 8(1/2), pp 54-66, January 2012.| Keywords: | kidney transplants, microarray, machine learning, feature selection, interaction networks, hub genes, bioinformatics, kidney rejection, medical informatics |
| Category: | In Journal |
| Web Links: | DOI |
| Paper Link | |
| Related Publication(s): | Combining Gene Expression and Interaction Network Data to Improve Kidney Lesion Score Prediction |
BibTeX
@article{Moulavi+al:IJBRA12,
author = {Davoud Moulavi and Mohsen Hajiloo and Joerg Sander and Philip
Halloran and Russ Greiner},
title = {Combining Gene Expression and Interaction Network Data to Improve
Kidney Lesion Score Prediction},
Volume = "8",
Number = "1/2",
Pages = {54-66},
journal = {International Journal of Bioinformatics Research and Applications},
year = 2012,
}Last Updated: February 10, 2020Submitted by Sabina P
