Graph algorithms for integrated biological analysis, with applications to type 1 diabetes data

John D. Eblen, Ivan Gerling, Arnold M. Saxton, Jian Wu, Jay R. Snoddy, Michael A. Langston

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Graph algorithms can be effective tools for analyzing the immense data sets that frequently arise from high-throughput biological experiments. A major computational goal is to identify dense subgraphs, from which one can often infer some form of biological meaning. In this paper, new techniques are devised and analyzed in an effort to improve the quality and relevance of these subgraphs, and to extend the utility of clique-centric methods that may produce them. Using non-obese diabetic mice as a target organism, the paraclique algorithm is tested on transcriptomic data under various parameters in order to determine how it can best be tuned to applications. The use of proteomic anchors is also discussed in an effort to help guide subgraph selection in the presence of inhomogeneous data, which is an important but notoriously difficult problem in its own right.

Original languageEnglish (US)
Title of host publicationClustering Challenges in Biological Networks
Pages207-222
Number of pages16
StatePublished - Dec 1 2013
EventDIMACS Workshop on Clustering Problems in Biological Networks 2009 - Piscataway, NJ, United States
Duration: May 9 2006May 11 2006

Publication series

NameClustering Challenges in Biological Networks

Other

OtherDIMACS Workshop on Clustering Problems in Biological Networks 2009
CountryUnited States
CityPiscataway, NJ
Period5/9/065/11/06

Fingerprint

bioassay
organisms
mice

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics

Cite this

Eblen, J. D., Gerling, I., Saxton, A. M., Wu, J., Snoddy, J. R., & Langston, M. A. (2013). Graph algorithms for integrated biological analysis, with applications to type 1 diabetes data. In Clustering Challenges in Biological Networks (pp. 207-222). (Clustering Challenges in Biological Networks).

Graph algorithms for integrated biological analysis, with applications to type 1 diabetes data. / Eblen, John D.; Gerling, Ivan; Saxton, Arnold M.; Wu, Jian; Snoddy, Jay R.; Langston, Michael A.

Clustering Challenges in Biological Networks. 2013. p. 207-222 (Clustering Challenges in Biological Networks).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Eblen, JD, Gerling, I, Saxton, AM, Wu, J, Snoddy, JR & Langston, MA 2013, Graph algorithms for integrated biological analysis, with applications to type 1 diabetes data. in Clustering Challenges in Biological Networks. Clustering Challenges in Biological Networks, pp. 207-222, DIMACS Workshop on Clustering Problems in Biological Networks 2009, Piscataway, NJ, United States, 5/9/06.
Eblen JD, Gerling I, Saxton AM, Wu J, Snoddy JR, Langston MA. Graph algorithms for integrated biological analysis, with applications to type 1 diabetes data. In Clustering Challenges in Biological Networks. 2013. p. 207-222. (Clustering Challenges in Biological Networks).
Eblen, John D. ; Gerling, Ivan ; Saxton, Arnold M. ; Wu, Jian ; Snoddy, Jay R. ; Langston, Michael A. / Graph algorithms for integrated biological analysis, with applications to type 1 diabetes data. Clustering Challenges in Biological Networks. 2013. pp. 207-222 (Clustering Challenges in Biological Networks).
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