Journal
ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE
Volume 306, Issue -, Pages 5-18Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.entcs.2014.06.011
Keywords
Complex networks; Biological networks; Tumor diagnosys; Computational topology; Betti number
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Funding
- Future and Emerging Technologies (FET) programme within the Seventh Framework Programme (FP7) for Research of the European Commission, under the FP7 FET-Proactive Call 8 - DyMCS [FP7-ICT-318121]
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Complex networks equipped with topological data analysis are one of the promising tools in the study of biological systems (e.g. evolution dynamics, brain correlation, breast cancer diagnosis, etc...). In this paper, we propose jHoles, a new version of Holes, an algorithms based on persistent homology for studying the connectivity features of complex networks. jHoles fills the lack of an efficient implementation of the filtering process for clique weight rank homology. We will give a brief overview of Holes, a more detailed description of jHoles algorithm, its implementation and the problem of clique weight rank homology. We present a biological case study showing how the connectivity of epidermal cells changes in response to a tumor presence. The biological network has been derived from the proliferative, differentiated and stratum corneum compartments, and jHoles used for studying variation of the connectivity.
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