Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/ protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at

Motivation: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse, using experimental methods such as yeasttwo-hybrids and protein chips. Such data can be errorful and they often do not provide adequate functional information for the detected interactions; it is therefore useful to develop an in silico approach to further validate and annotate the detected protein interactions. Results: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale experimentally determined domain-domain interaction data do not exist; as such, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including rosetta stone sequences, protein interactions, and protein complexes. We show the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. Contact:

Abstract. The interpretation of large-scale protein network data depends on our ability to identify significant sub-structures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths in graphs to the problem of identifying pathways in protein interaction networks. We present linear-time algorithms for finding paths in networks under several biologically-motivated constraints. We apply our methodology to search for protein pathways in the yeast protein-protein interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in less than two hours. 1

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ABSTRACT Protein–protein interactions play a key role in many biological systems. High-throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false-positive and falsenegative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co-complex relationship, and (3) pathway co-membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity-based k-Nearest-Neighbor,

gene expression

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Motivation: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. Results: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ‘leave-one-out ’ approach we find average success rates between 20 and 40 % for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. Availability: A reference implementation and a table with candidate interacting partners for each yeast protein are available

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All genomic feature data used in this study can be downloaded at