Abstract
Protein complexes are groups of interacting proteins unified by a common biological function. Identifying complexes amid a network of thousands of interacting proteins poses a difficult computational challenge. Traditional approaches to this problem rely on clique-like topography in order to identify complexes. Supervised learning is an alternative approach that leverages real-valued data in order to extract the features of protein complexes and identify candidates that do not conform to traditional, dense clique structures. SCODE (Supervised Complex Detection), an application for the Cytoscape App Store, implements a supervised learning algorithm for the detection of protein complexes in protein-protein interaction networks.
Highlights
Protein-protein interaction (PPI) networks provide information about the biochemical relationships in a cell’s molecular machinery
We demonstrate the app using training/evaluation datasets constructed from two sources: the CYC2008 catalogue of manually curated protein complexes[4], and the TAP06 dataset of complexes screened using affinity purification and mass spectrometry[5], which were each filtered to a random sampling of 50 complexes with 4–6 nodes
Summary SCODE expands the detection of protein complexes in weighted PPI networks by applying a supervised learning algorithm with a set of known training complexes
Summary
Protein-protein interaction (PPI) networks provide information about the biochemical relationships in a cell’s molecular machinery. Each node in the network represents a protein, with edges connecting proteins that physically interact with one another. Complexes are clusters of interacting proteins in the network which are together responsible for some biological function. The discovery of complexes in a PPI network has implications in the study of the molecular basis of diseases, drug targets, and biological pathways[1]. Existing tools for complex detection in PPI networks rely on assumptions about a common topography observed among most protein complexes. These tools often assume that protein complexes can be identified based on the density of interactions (edges) among the set of its nodes. Real-valued data indicate that a variety of other features, including but not limited to edge density, are typical of complexes among PPI networks[3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
