An integer programming framework for inferring disease complexes from network data.
Unraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process.



Here, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge.



The datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zip



roded@post.tau.ac.il.
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Arnon
A
Mazza
Max Delbrück Center for Molecular Medicine, Berlin, Germany.
Konrad
K
Klockmeier
Max Delbrück Center for Molecular Medicine, Berlin, Germany.
Erich
E
Wanker
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Roded
R
Sharan
D000465:Algorithms; D060066:Protein Interaction Maps; D011506:Proteins; D012984:Software
D011506:Proteins