Title

Authors

Affiliation

¹Louisville Collegiate School and Department of Mathematics, University of Louisville; ²Department of Bioinformatics and Biostatistics, University of Louisville, ³Department of Mathematics, University of Louisville, Louisville

Email

ryan.gill@louisville.edu; *Corresponding author

Article Type

Hypothesis

Date

Received September 30, 2014; Revised October 04, 2014; Accepted October 05, 2014; Published October 30, 2014

Various methods to determine the connectivity scores between groups of proteins associated with lung adenocarcinoma are examined. Proteins act together to perform a wide range of functions within biological processes. Hence, identification of key proteins and their interactions within protein networks can provide invaluable information on disease mechanisms. Differential network analysis provides a means of identifying differences in the interactions among proteins between two networks. We use connectivity scores based on the method of partial least squares to quantify the strength of the interactions between each pair of proteins. These scores are then used to perform permutation-based statistical tests. This examines if there are significant differences between the network connectivity scores for individual proteins or classes of proteins. The expression data from a study on lung adenocarcinoma is used in this study. Connectivity scores are computed for a group of 109 subjects who were in the complete remission and as well as for a group of 51 subjects whose cancer had progressed. The distributions of the connectivity scores are similar for the two networks yet subtle but statistically significant differences have been identified and their impact discussed.

Keywords

protein-protein, networks, lung adenocarcinoma, expression data, protein-protein interaction, association networks, lung adenocarcinoma.

Citation

Datta et al. Bioinformation 10(10): 647-651 (2014)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.