Predicting interactions in protein networks by completing defective cliques | |
Haiyuan Yu, Alberto Paccanaro, Valery Trifinov and Mark Gerstein | |
Abstract Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pair-wise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. | |
Scripts and binaries | |
       C scripts | |
  | 1. Find maximal cliques |
  | 2. Complete defective cliques |
  | 3. All C scripts |
       Binaries | |
  | 1. Convert input datasets into appropirate format |
  | 2. Find maximal cliques |
  | 3. Complete defective cliques |
  | Please note that you first have to convert your input file into a binary file using "convert2mtx" before you can ran "maxcliq" or "dcc". In other words, "maxcliq" and "dcc" only take the output from "convert2mtx" as input!!! |
Datasets | |
  | 1. Universal dataset (56x56) for Gold-standards |
  | 2. Universal dataset (56x56) for large-scale experiments |
  | 3. Protein names for the universal dataset |
  | 4. Large-scale experimental dataset |
  | 5. Help file for the datasets |
Last modified on June 23rd, 2006 |