Using fixed point theorems to model the binding in protein-protein interactions
The binding in protein-protein interactions exhibits a kind of biochemical stability in cells. The mathematical notion of fixed points also describes stability. A point is a fixed point if it remains unchanged after a transformation by a function. Many points may not be a fixed point, but they may approach a stable status after multiple steps of transformation. In this paper, we define a point as a protein motif pair consisting of two traditional protein motifs. We propose a function and propose a method to discover stable motif pairs of this function from a large protein interaction, sequence data set. There are many interesting properties for this function (for example, the convergence). Some of them are useful for gaining much efficiency in the discovery of those stable motif pairs; some are useful for explaining why our proposed fixed point theorems are a good way to model the binding of protein interactions. Our results are also compared to biological results to elaborate the effectiveness, of our method.

title: Relative risk and odds ratio: a data mining perspective
abstract: We are often interested to test whether a given cause has a given effect. If we cannot specify the nature of the factors involved, such tests are called model-free studies. There are two major strategies to demonstrate associations between risk factors (ie. patterns) and outcome phenotypes (ie. class labels). The first is that of prospective study designs, and the analysis is based on the concept of "relative risk": What fraction of the exposed (ie. has the pattern) or unexposed (ie. lacks the pattern) individuals have the phenotype (ie. the class label)? The second is that of retrospective designs, and the analysis is based on the concept of "odds ratio": The odds that a case has been exposed to a risk factor is compared to the odds for a case that has not been exposed. The efficient extraction of patterns that have good relative risk and/or odds ratio has not been previously studied in the data mining context. In this paper, we investigate such patterns. We show that this pattern space can be systematically stratified into plateaus of convex spaces based on their support levels. Exploiting convexity, we formulate a number of sound and complete algorithms to extract the most general and the most specific of such patterns at each support level. We compare these algorithms. We further demonstrate that the most efficient among these algorithms is able to mine these sophisticated patterns at a speed comparable to that of mining frequent closed patterns, which are patterns that satisfy considerably simpler conditions.

title: Discovery of Binding Motif Pairs from Protein Complex Structural Data and Protein Interaction Sequence Data
abstract: Unravelling the underlying mechanisms of protein interactions requires knowledge about the interactions' binding sites. In this paper, we use a novel concept, binding motif pairs, to describe binding sites. A binding motif pair consists of two motifs each derived from one side of the binding protein sequences. The discovery is a directed approach that uses a combination of two data sources: 3-D structures of protein complexes and sequences of interacting proteins. We first extract maximal contact segment pairs from the protein complexes' structural data. We then use these segment pairs as templates to sub-group the interacting protein sequence dataset, and conduct an iterative refinement to derive significant binding motif pairs. This combination approach is efficient in handling large datasets of protein interactions. From a dataset of 78,390 protein interactions, we have discovered 896 significant binding motif pairs. The discovered motif pairs include many novel motif pairs as well as motifs that agree well with experimentally validated patterns in the literature.

title: Discovery of stable and significant binding motif pairs from PDB complexes and protein interaction datasets
abstract: 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: Scalable audio streaming over the internet with network-aware rate-distortion optimization
abstract: Reliable audio streaming over the unreliable Internet is a challenging task. Because during a connected session, the bandwidth can vary greatly and the data packets can be lost in the transmission, it is difficult to guarantee a smooth quality of the delivered audio over the Internet. In this work, a system for streaming of scalable coded audio over the Internet is proposed. Scalable audio coding generates a bitstream consisting a number of data units (DUs), where a subset of the DUs can be extracted to reconstruct audio at a lower quality level. A network aware ratedistortion optimization model is proposed so that the server can easily adapt to the network bandwidth fluctuation by sending less packets when the network is congested. The server can also deal with severe packet loss by only retransmitting the more important data packets. By using the scalable audio coding and network aware delivery scheme, the quality of the audio streaming over severe network condition is improved dramatically.

title: Critical Damage Reporting in Intelligent Sensor Networks
abstract: In this paper, we present a Top-Down/Bottom-Up (TDBU) design approach for critical damage reporting in intelligent sensor networks This approach is a minimal hierarchical decomposition of the problem, which seeks a balance between achievability and complexity Our simulated environment models two-dimensional square cells as autonomous agents which sense their local environment, reporting critical damage as rapidly as possible to a report delivery site (portal) by using only the adjacent-cell communication links The global goal is to design agent properties which will allow the multi-agent network to detect critical damage anywhere on the network and to communicate this information to a portal whose location is unknown to the agents We apply a TDBU approach together with genetic algorithms (GA) to address the global goal Simulations show that our system can successfully report critical damage much better than random methods.

title: Applying Association Rules for Interesting Recommendations Using Rule Templates
abstract: In this paper, we propose a new method of applying association rules for recommendation systems. Association rule algorithms are used to discover associations among items in transaction datasets. However, applying association rule algorithms directly to make recommendations usually generates too many rules; thus, it is difficult to find interesting recommendations for users among so many rules. Rule templates define certain types of rules; therefore, they are one of the interestingness measures that reduce the number of rules that do not interest users. We describe a new method. By defining more appropriate rule templates, we are able to extract interesting rules for users in a recommendation system. Experimental results show that our method increases the accuracy of recommendations.

title: Observations on versioning of off-the-shelf components in industrial projects (short paper)
abstract: Using OTS (Off-The-Shelf) components in software projects has become increasing popular in the IT industry. After project managers opt for OTS components, they can decide to use COTS (Commercial-Off-The-Shelf) components or OSS (Open Source Software) components. We have done a series of interviews and surveys to document and understand industrial practice with OTS-based development in Norwegian, German, and Italian IT industry. The perspective is that of a software architect or system integrator, not a developer or maintainer of such components. The study object is a completed development project using one or several OTS components. This paper reports on the versioning aspects of OTS components in such projects. We found that one third of the COTS components actually provided source code, in addition to all OSS components. However, OTS components were seldom modified (i.e. re-used "as-is"), even if source code was available. Although backward compatibility of new releases did not cause noticeable problems for a single OTS component, using several different OTS components in a project caused difficulties in maintenance planning of asynchronous releases and system integration of new releases. Several new research questions have been formulated based on the results of this study.

title: (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
abstract: Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.