Close
Help
signup_email_alerts
Need Help?
Search



Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks

Submit a Paper


Libertas Analytics

Download Paper
Send to Endnote

4252 Article Views

Authors: Dirk Koschützki and Falk Schreiber

Publication Date: 15 May 2008

Journal: Gene Regulation and Systems Biology

Citation: Gene Regulation and Systems Biology 2008:2 193-201

GRSB journal

262,591 Article Views

6,654,881 Libertas Article Views

More Statistics

Dirk Koschützki1,2 and Falk Schreiber1,3

1Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany. 2Department of Computer and Electrical Engineering, Furtwangen University of Applied Sciences, Robert-Gerwig-Platz 1, 78120 Furtwangen, Germany. 3Institute for Computer Science, Martin-Luther-University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle, Germany.

Abstract

The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory network. We show that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks.

Post a Comment

x close

Discussion Add A Comment
No comments yet...Be the first to comment.


share on

Related Categories

Readers of this Article also read

Our Service Promise

  • Prompt Processing (Less Than 3 Weeks)
  • Fair & Comprehensive Peer Review
  • Professional Author Service
  • Leading Editors in Chief
  • Extensive Indexing
  • High Readership & Impact
  • What Your Colleagues Say

Quick Links

Follow Us We make it easy to find new research papers. RSS Feeds Email Alerts Twitter

BROWSE CATEGORIES
Our Testimonials
It's a great experience publishing with Biomedical Informatics Insights. I am particularly impressed with the in-depth and constructive comments provided by the reviewers within such a short time-frame. The typesetting was not only prompt, but most importantly, effective. In fact, this was among the very few publication experiences that I have had when no correction was needed in the author proofs. I highly recommend Biomedical Informatics Insights to both readers and prospective authors in our research community.
Dr Chun Hsi Huang (Computer Science and Engineering, University of Connecticut) What Your Colleagues Say