Abstract not found

Almost every cellular process requires the interactions of pairs or larger complexes of proteins. High throughput protein-protein interaction (PPI) data have been generated using techniques such as the yeast two-hybrid systems, mass spectrometry method, and many more. Such data provide us with a new perspective to predict protein functions and to generate protein-protein interaction networks, and many recent algorithms have been developed for this purpose. However, PPI data generated using high throughput techniques contain a large number of false positives. In this paper, we have proposed a novel method to evaluate the support for PPI data based on gene ontology information. If the semantic similarity between genes is computed using gene ontology information and using Resnik’s formula, then our results show that we can model the PPI data as a mixture model predicated on the assumption that true proteinprotein interactions will have higher support than the false positives in the data. Thus semantic similarity between genes serves as a metric of support for PPI data. Taking it one step further, new function prediction approaches are also being proposed with the help of the proposed metric of the support for the PPI data. These new function prediction approaches outperform their conventional counterparts. New evaluation methods are also proposed. 1.

Abstract—Protein-protein interactions (PPI) refer to the associations between proteins and the study of these associations. Several approaches have been used to address the problem of predicting PPI. Some of them are based on biological features extracted from a protein sequence (such as, amino acid composition, GO terms, etc.); others use relational and structural features extracted from the PPI network, which can be represented as a graph. Our approach falls in the second category. We adapt a general approach to graph feature extraction that has previously been applied to collaborative recommendation of friends in social networks. Several structural features are identified based on the PPI graph and used to learn classifiers for predicting new interactions. Two datasets containing Saccharomyces cerevisiae PPI are used to test the proposed approach. Both these datasets were assembled from the Database of Interacting Proteins (DIP). We assembled the first data set directly from DIP in April 2006, while the second data set has been used in previous studies, thus making it easy to compare our approach with previous approaches. Several classifiers are trained using the structural features extracted from the interactions graph. The results show good performance (accuracy, sensitivity and specificity), proving that the structural features are highly predictive with respect to PPI.

© 2010 Mohamed et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License

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This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Prediction of glycosylation sites using random forests

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

Abstract: Machine learning methods are often used to predict Protein-Protein Interactions (PPI). It is common to develop methods using known PPI from well-characterised reference organisms, drawing from that organism data for inferring a predictive model and evaluating the model. We present evidence that this practice does not give a meaningful indication of the model’s performance on genetically distinct organisms. We conclude that this practice cannot be applied to proteins inferred from the genetic sequence of a novel organism for which no PPI data is available, and that there is need for evaluating such methods on organisms distinct from their training organisms.