Woo-Hyuk Jang

Protein complex prediction based on simultaneous protein interaction network

MOTIVATION The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions. RESULTS In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions. AVAILABILITY http://code.google.com/p/simultaneous-pin/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Radio Map Update Automation for WiFi Positioning Systems

This paper presents a novel method to reduce the recalibration costs of a radio map by automatically updating the radio map. The appearance frequencies of access points (APs) detected from user feedback data are mainly used for the update. The proposed method appeared superior to previous methods, especially in its ability to update newly installed APs in the radio map. According to the experiment conducted for the radio map of 233 Seoul subway stops, the proposed method was effective for updating APs with weak as well as strong signal strengths.

A Computational Model for Predicting Protein Interactions Based on Multidomain Collaboration

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.

Domain combination based protein-protein interaction possibility ranking method

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However most of the techniques still have many limitations to be used in real fields. They usually suffer from low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the reevaluation, we have found that the accuracy of the prediction is improved as the size of non-interacting set of protein pairs is increased. When the size of non-interacting set of protein pairs is increased to 20 times bigger than that of interacting set of protein pairs in learning sets, 84% sensitivity and 75% specificity were achieved in yeast organism. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the accuracy of the prediction in case of the protein pair group having the matching PIP values in the interacting or non-interacting PIP distributions.

Communication Protocols and Message Formats for BLAST Parallelization on Cluster Systems

With the widespread use of BLAST, many parallel versions of BLAST on cluster systems are announced, but little work has been done for the parallel execution in the search for individual query sequence on BLAST on cluster systems. Since we can improve not only throughput but also response time, the techniques for parallel execution of BLAST on cluster systems in the search for individual query sequence deserve to be developed. This paper develops communication protocols and message formats to reduce the communication overheads for the parallel execution of BLAST in the search for individual query sequence on cluster systems. The developed communication protocols and message formats are implemented on a new version of BLAST on cluster systems. The new version of BLAST is named Hyper-BLAST in this paper. In this paper, we also measured the throughput and response time of Hyper-BLAST on various cluster systems. It turned out that considerable performance improvement of BLAST on cluster systems can be achieved through parallel execution in the search for individual query sequence on small or middle-sized cluster systems. On 1-way 64-node system, Hyper-BLAST achieved scalable speedup up to 63 processors for 1000-5000 length query size.

Modeling of multi domain contribution to protein interaction

Recently, several domain based computational models for predicting protein-protein interactions (PPIs) have been proposed. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multi domain interaction) will actually interact for a predicted protein interaction. To solve this, we developed a computational model to predict PPIs using information of intraprotein domain cohesions and inter-protein domain or DC coupling interactions. A method to identify the primary interacting DC pair is also incorporated into the model for inferring actual participants in a predicted interaction. Our method made a significant improvement in PPI prediction accuracy, and the primary interacting DC pair identification turned out valid specifically in predicting multi domain protein interactions.

A Protein-Protein Interaction Prediction Method Embracing Intra-protein Domain Cohesion Information

Recently, many computational methods for predict-ing protein-protein interaction (PPI) have been devel-oped by utilizing domain-domain interaction or asso-ciated information. However, most of the methods lack of reflecting the collaboration effect of multiple do-mains to the prediction of PPI. In this paper, we devel-op a computational model that considers not only inter relationship between protein pair but also the intra-domain functional cohesion effect in PPI. In the computational model, a value assigning method to reflect the intra and inter collaboration devised and the computed values are stored in Interaction Significance (IS) matrix. Then an equation for PPI prediction is devised on IS matrix. For S. cerevisiae PPI data from DIP, MINT and IntAct, domain data from Pfam-A, the prediction method achieved 73.91% and 92.02% sensitivity and specificity respectively.