Tae-Ho Kang

Identification of Diseasomal Proteins from Atopy-Related Disease Network

In this study, we employed the idea that disease-related proteins tend to be work as an important factor for architecture of the disease network. We initially obtained 43 atopy-related proteins from the Online Mendelian Inheritance in Man (OMIM) and then constructed atopy-related protein interaction network. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived three diseasomal proteins, CCR5, CCL11, and IL/4R. Although we use the relatively small subnetwork, an atopy-related disease network, it is sufficient that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

Efficient Mobile P2P Structure for Content Search Services

In this paper, we propose the mobile P2P structure supporting content searches for mobile peers efficiently. The proposed mobile P2P structure is a 3-tier structure which consists of a mobile peer, a mobile super peer, and a stationary super peer to reduce the content search cost of mobile P2P service. For content searches, mobile peer searches content in the communication range and performs hierarchical content searches which is using mobile super peer, stationary super peer for expansion of query region. In order to support hierarchial content searches and the continuity of services according to peer mobilities, peer`s join/leave processes are explicitly stored by supporting message structures to the upper layer It is shown through experimental evaluation that the proposed structure improves about 32% contents search performance over the existing 2-tier structure. Since it also reduces the messages transferred to the stationary super peers, it reduced about 25% search loads of them.

Fucntional Prediction Method for Proteins by using Modified Chi-square Measure

Functional prediction of unannotated proteins is one of the most important tasks in yeast genomics. Analysis of a protein-protein interaction network leads to a better understanding of the functions of unannotated proteins. A number of researches have been performed for the functional prediction of unannotated proteins from a protein-protein interaction network. A chi-square method is one of the existing methods for the functional prediction of unannotated proteins from a protein-protein interaction network. But, the method does not consider the topology of network. In this paper, we propose a novel method that is able to predict specific molecular functions for unannotated proteins from a protein-protein interaction network. To do this, we investigated all protein interaction DBs of yeast in the public sites such as MIPS, DIP, and SGD. For the prediction of unannotated proteins, we employed a modified chi-square measure based on neighborhood counting and we assess the prediction accuracy of protein function from a protein-protein interaction network.

Mining Maximal Frequent Contiguous Sequences in Biological Data Sequences

Biological sequences such as DNA sequences and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological dataset with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with the fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. As the result, the experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

Protein Function Finding Systems through Domain Analysis on Protein Hub Network

We propose a protein function finding algorithm that is able to predict specific molecular function for unannotated proteins through domain analysis from protein-protein network. To do this, we first construct protein-protein interaction(PPI) network in Saccharomyces cerevisiae from MIPS databases. The PPI network(proteins; 3,637, interactions; 10,391) shows the characteristics of a scale-free network and a hierarchical network that proteins with a number of interactions occur in small and the inherent modularity of protein clusters. Protein-protein interaction databases obtained from a Y2H(Yeast Two Hybrid) screen or a composite data set include random false positives. To filter the database, we reconstruct the PPI networks based on the cellular localization. And then we analyze Hub proteins and the network structure in the reconstructed network and define structural modules from the network. We analyze protein domains from the structural modules and derive functional modules from them. From the derived functional modules with high certainty, we find tentative functions for unannotated proteins.

Analysis of Essential Proteins in Protein-Protein Interaction Networks

Protein interaction network contains a small number of highly connected protein, denoted hub and many destitutely connected proteins. Recently, several studies described that a hub protein is more likely to be essential than a non-hub protein. This phenomenon called as a centrality-lethality rule. This nile is widely credited to exhibit the importance of hub proteins in the complex network and the significance of network architecture as well. To confirm whether the rule is accurate, we Investigated all protein interaction DBs of yeast in the public sites such as Uetz, Ito, MIPS, DIP, SGB, and BioGRID. Interestingly, the protein network shows that the rule is correct in lower scale DBs (e.g., Uetz, Ito, and DIP) but is not correct in higher scale DBs (e.g., SGD and BioGRID). We are now analyzing the features of networks obtained from the SGD and BioGRD and comparing those of network from the DIP.

Similarity Measurement with Interestingness Weight for Improving the Accuracy of Web Transaction Clustering

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users` interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method