Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

Mathematical Citation Quotient (MCQ) 2018: 0.02

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 9, Issue 1

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Sub-Modular Resolution Analysis by Network Mixture Models

Elisabetta Marras / Antonella Travaglione / Enrico Capobianco
Published Online: 2010-04-09 | DOI: https://doi.org/10.2202/1544-6115.1523

Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to a certain degree of coverage and accuracy, due to both experimental and computational limitations. Therefore, we need to infer from the measured interactomes about real interactomes as much as we infer from samples relative to a reference population. In order to exploit additional information sources, it is common to integrate multiple omic data and pursue method fusion. Particularly after the advent of high-throughput technologies, the increased complexity of data-intensive applications has determined an important role for network inference. Consequently, advances in spectral clustering, community detection algorithms and modularity optimization methods have been proposed, according to both deterministic and probabilistic solutions. We have considered the two kinds of approaches, and applied some of the available methods to two human interactomes obtained from high-throughput small-scale experiments and mass spectrometry measurements. The main motivation of this study is refining the resolution spectrum at which protein modularity maps can be studied. First, we started by a coarse-grained interactome decomposition through core and community structures, and by applying sub-sampling to the interactome adjacency matrix. Then, we switched to stochastic methods to uncover fine-grained interactome components, and applied both variational and mixture statistical models. Lastly, we integrated our analysis with the biological validation of the retrieved modules. Overall, the proposed approach shows potential for calibrating modularity detection in protein interactomes at different resolutions.

Keywords: biological networks; interactome modularity; mixture models; variational learning; biological validation

About the article

Published Online: 2010-04-09


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1523.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Antonio Mora, Rosa Sicari, Lauro Cortigiani, Clara Carpeggiani, Eugenio Picano, and Enrico Capobianco
Royal Society Open Science, 2015, Volume 2, Number 2, Page 140270
[2]
W. P. Kelly and M. P. H. Stumpf
Bulletin of Mathematical Biology, 2012, Volume 74, Number 2, Page 356
[3]
Enrico Capobianco
Journal of Clinical Medicine, 2019, Volume 8, Number 5, Page 664
[4]
Enrico Capobianco
Computational and Structural Biotechnology Journal, 2014, Volume 11, Number 19, Page 123
[5]
Marco Dominietto, Nicholas Tsinoremas, and Enrico Capobianco
Molecular Oncology, 2015, Volume 9, Number 1, Page 1
[6]
Enrico Capobianco
Big Data and Information Analytics, 2016, Volume 1, Number 2/3 , Page 163
[7]
Nicola Ianuale, Duccio Schiavon, and Enrico Capobianco
Journal of Management Analytics, 2015, Volume 2, Number 4, Page 285
[8]
Enrico Capobianco
Systems Biomedicine, 2013, Volume 1, Number 3, Page 161
[9]
Enrico Capobianco
ISRN Genomics, 2013, Volume 2013, Page 1
[10]
Enrico Capobianco
ISRN Biomathematics, 2012, Volume 2012, Page 1

Comments (0)

Please log in or register to comment.
Log in