Computational Science Technical Note CSTN-136

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Applying Enumerative, Spectral and Hybrid Graph Analyses to Biological Network Data

K.A. Hawick

Archived July 2011

Abstract

Many biological data sets are expressable as complex networks of genetic information or protein structural information. As such they can be analysed and interpreted using enumerative graph methids for calculating their static and structural properties. We consider enumerative as well as spectral methods for analysing graphs of biological networks. We review computational algorithms for determining properties such as: path legths; component cluster distribution; circuits and community structure as well as examining some heuristics and hybrid algorithms combining these approaches. We discuss some of the common properties they reveal for bio-network data. We apply these methods to some public domain biological network data and discuss the computational performance and scaling of these approaches to very large bio-network sizes.

Keywords: bio-networks; complexity; clusters; spectral methods; community structure.

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@INPROCEEDINGS{CSTN-136,
  author = {K. A. Hawick},
  title = {Applying Enumerative, Spectral and Hybrid Graph Analyses to Biological
	Network Data},
  booktitle = {Int. Conf. on Computational Intelligence and Bioinformatics (CIB
	2011)},
  year = {2011},
  pages = {89-96},
  address = {Pittsburgh, USA},
  month = {7-9 November},
  publisher = {IASTED},
  doi = {10.2316/P.2011.753-040},
  institution = {Computer Science, Massey University, Albany, Auckland, New Zealand},
  timestamp = {2011.08.23}
}


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