Discovery of stable and significant binding motif pairs from PDB complexes and protein interaction datasets
Discovery of binding sites is important in the study of protein-protein interactions. In this paper, we introduce stable and significant motif pairs to model protein-binding sites. The stability is the pattern's resistance to some transformation. The significance is the unexpected frequency of occurrence of the pattern in a sequence dataset comprising known interacting protein pairs. Discovery of stable motif pairs is an iterative process, undergoing a chain of changing but converging patterns. Determining the starting point for such a chain is an interesting problem. We use a protein complex dataset extracted from the Protein Data Bank to help in identifying those starting points, so that the computational complexity of the problem is much released.



We found 913 stable motif pairs, of which 765 are significant. We evaluated these motif pairs using comprehensive comparison results against random patterns. Wet-experimentally discovered motifs reported in the literature were also used to confirm the effectiveness of our method.



http://sdmc.i2r.a-star.edu.sg/BindingMotifPairs.

title: A Correspondence Between Maximal Complete Bipartite Subgraphs and Closed Patterns
abstract: For an undirected graph G without self-loop, we prove: (i) that the number of closed patterns in the adjacency matrix of G is even; (ii) that the number of the closed patterns is precisely double the number of maximal complete bipartite subgraphs of G; (iii) that for every maximal complete bipartite subgraph, there always exists a unique pair of closed patterns that matches the two vertex sets of the subgraph. Therefore, we can enumerate all maximal complete bipartite subgraphs by using efficient algorithms for mining closed patterns which have been extensively studied in the data mining field.

title: Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models
abstract: This paper treats a multiresolution hidden Markov model for classifying images. Each image is represented by feature vectors at several resolutions, which are statistically dependent as modeled by the underlying state process, a multiscale Markov mesh. Unknowns in the model are estimated by maximum likelihood, in particular by employing the expectation-maximization algorithm. An image is classified by finding the optimal set of states with maximum a posteriori probability. States are then mapped into classes. The multiresolution model enables multiscale information about context to be incorporated into classification. Suboptimal algorithms based on the model provide progressive classification that is much faster than the algorithm based on single-resolution hidden Markov models.

title: Context based multiscale classification of images
abstract: This paper presents an algorithm to segment images into four classes: background, photograph, text and graph. There are two important aspects about the algorithm. The first is that the algorithm takes a multiscale approach, which adaptively classifies an image at different resolutions. The multiscale structure enables accurate classification at class boundaries as well as fast classification overall. The second is that the context information, which is accumulated in the process of classification, is used to improve the classification accuracy.

title: Mining risk patterns in medical data
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title: Efa: an efficient content routing algorithm in large peer-to-peer overlay networks
abstract: An important issue for peer-to-peer application is to locate content within the network. There are many existing solutions to this problem, however, each of them addresses different aspects and each has its deficiencies. We focus on the unstructured peer-to-peer scenario and present a constrained flooding routing algorithm, Efa, which overcomes some of the deficiencies of those existing strategies. Efa performs application level broadcasting in a potentially very large peer-to-peer network overlaid on the Internet. Efa is completely decentralized and self-organized. It is a more scalable alternative to flooding, which is commonly used in unstructured peer-to-peer systems. Utilizing just a small amount of topology info, Efa is almost as simple as flooding, but it is much more efficient and scalable.

title: A Rough Set Based Model to Rank the Importance of Association Rules
abstract: Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based process by which a rule importance measure is calculated for association rules to select the most appropriate rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule among the rule sets. Rule importance is different from rule interestingness in that it does not consider the predefined knowledge on what kind of information is considered to be interesting. The experimental results show our method reduces the number of rules generated and at the same time provides a measure of how important is a rule.