MOTIVATION: It has been recognized that the topology of molecular networks provides information about the certainty and nature of individual interactions. Thus, network motifs have been used for predicting missing links in biological networks and for removing false positives. However, various different measures can be inferred from the structure of a given network and their predictive power varies depending on the task at hand. RESULTS: Herein, we present a systematic assessment of seven different network features extracted from the topology of functional genetic networks and we quantify their ability to classify interactions into different types of physical protein associations. Using machine learning, we combine features based on network topology with non-network features and compare their importance of the classification of interactions. We demonstrate the utility of network features based on human and budding yeast networks; we show that network features can distinguish different sub-types of physical protein associations and we apply the framework to fission yeast, which has a much sparser known physical interactome than the other two species. Our analysis shows that network features are at least as predictive for the tasks we tested as non-network features. However, feature importance varies between species owing to different topological characteristics of the networks. The application to fission yeast shows that small maps of physical interactomes can be extended based on functional networks, which are often more readily available. AVAILABILITY AND IMPLEMENTATION: The R-code for computing the network features is available from www.cellularnetworks.org
MOTIVATION: It has been recognized that the topology of molecular networks provides information about the certainty and nature of individual interactions. Thus, network motifs have been used for predicting missing links in biological networks and for removing false positives. However, various different measures can be inferred from the structure of a given network and their predictive power varies depending on the task at hand. RESULTS: Herein, we present a systematic assessment of seven different network features extracted from the topology of functional genetic networks and we quantify their ability to classify interactions into different types of physical protein associations. Using machine learning, we combine features based on network topology with non-network features and compare their importance of the classification of interactions. We demonstrate the utility of network features based on human and budding yeast networks; we show that network features can distinguish different sub-types of physical protein associations and we apply the framework to fission yeast, which has a much sparser known physical interactome than the other two species. Our analysis shows that network features are at least as predictive for the tasks we tested as non-network features. However, feature importance varies between species owing to different topological characteristics of the networks. The application to fission yeast shows that small maps of physical interactomes can be extended based on functional networks, which are often more readily available. AVAILABILITY AND IMPLEMENTATION: The R-code for computing the network features is available from www.cellularnetworks.org