Learning of Protein Interaction Network

Protein-protein interactions (PPI) play a key role in determining the outcome of most cellular processes. Correctly identifying and characterizing protein interactions and the networks they comprise is critical for understanding the molecular mechanisms within the cell. Large-scale biological experimental methods can directly and systematically detect the set of interacting proteins within an organism. Unfortunately, the resulting datasets are often incomplete and exhibit high false positive and false negative rates. In addition to the direct experimental data, a number of large biological datasets also provide indirect evidence about protein-interaction relationships. Thus computational approaches could be utilized to combine multiple information sources in order to predict the sets of interacting protein pairs and identify important biological substructures in this network. In this dissertation, we first carry out a systematic study of the efficacy of using supervised learning methods to integrate direct and indirect biological evidence for predicting pairwise protein interactions. The results indicate that the utility of information, the way the data is encoded as features, the target types of protein interactions and the computational approaches used are all significant for predicting such interactions. We then propose four learning algorithms for deriving PPI networks from different perspectives. (I) A combined computational and experimental approach is proposed for predicting interaction partners of human membrane receptors. The random forest binary classifier is employed to determine if a potential receptor-human pair interacts or not. Biological feedback is used to optimize feature encoding and improve the accuracy of predictions. The resulting receptor PPI network is then analyzed through graph property analysis, graph module identification and protein-family related network pattern search. Several novel predictions are further experimentally validated. Our proposed framework shows that focusing on specific subnetworks generates better predictions. The predicted network provides the most reliable dataset on the network of interactions involving human membrane receptors to date. (II) Considering that PPI networks are highly sparse graphs and there is no large negative reference set (non-interacting pairs) available, we design a ranking approach to identify