Display Settings:


Send to:

Choose Destination
See comment in PubMed Commons below
J Comput Biol. 2012 Dec;19(12):1284-94. doi: 10.1089/cmb.2012.0195.

Identifying complexes from protein interaction networks according to different types of neighborhood density.

Author information

  • 1Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843-3112, USA.


To facilitate the realization of biological functions, proteins are often organized into complexes. While computational techniques are used to predict these complexes, detailed understanding of their organization remains inadequate. Apart from complexes that reside in very dense regions of a protein interaction network in which most algorithms are able to identify, we observe that many other complexes, while not residing in very dense regions, reside in regions with low neighborhood density. We develop an algorithm for identifying protein complexes by considering these two types of complexes separately. We test our algorithm on a few yeast protein interaction networks, and show that our algorithm is able to identify complexes more accurately than existing algorithms. A software program NDComplex for implementing the algorithm is available at http://faculty.cse.tamu.edu/shsze/ndcomplex.

[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Mary Ann Liebert, Inc. Icon for PubMed Central
    Loading ...
    Write to the Help Desk