16198762
Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale
MOTIVATION
Protein-protein interaction, mediated by protein interaction sites, is intrinsic to many functional processes in the cell. In this paper, we propose a novel method to discover patterns in protein interaction sites. We observed from protein interaction networks that there exist a kind of significant substructures called interacting protein group pairs, which exhibit an all-versus-all interaction between the two protein-sets in such a pair. The full-interaction between the pair indicates a common interaction mechanism shared by the proteins in the pair, which can be referred as an interaction type. Motif pairs at the interaction sites of the protein group pairs can be used to represent such interaction type, with each motif derived from the sequences of a protein group by standard motif discovery algorithms. The systematic discovery of all pairs of interacting protein groups from large protein interaction networks is a computationally challenging problem. By a careful and sophisticated problem transformation, the problem is solved using efficient algorithms for mining frequent patterns, a problem extensively studied in data mining.


RESULTS
We found 5349 pairs of interacting protein groups from a yeast interaction dataset. The expected value of sequence identity within the groups is only 7.48%, indicating non-homology within these protein groups. We derived 5343 motif pairs from these group pairs, represented in the form of blocks. Comparing our motifs with domains in the BLOCKS and PRINTS databases, we found that our blocks could be mapped to an average of 3.08 correlated blocks in these two databases. The mapped blocks occur 4221 out of total 6794 domains (protein groups) in these two databases. Comparing our motif pairs with iPfam consisting of 3045 interacting domain pairs derived from PDB, we found 47 matches occurring in 105 distinct PDB complexes. Comparing with another putative domain interaction database InterDom, we found 203 matches.


AVAILABILITY
http://research.i2r.a-star.edu.sg/BindingMotifPairs/resources.


SUPPLEMENTARY INFORMATION
http://research.i2r.a-star.edu.sg/BindingMotifPairs and Bioinformatics online.
1613791
Relative risk and odds ratio: a data mining perspective
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.
6463173
Discovery of Binding Motif Pairs from Protein Complex Structural Data and Protein Interaction Sequence Data
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.
15144335
Discovery of stable and significant binding motif pairs from PDB complexes and protein interaction datasets
MOTIVATION
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.


RESULTS
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.


SUPPLEMENTARY INFORMATION
http://sdmc.i2r.a-star.edu.sg/BindingMotifPairs.
2818605
A Correspondence Between Maximal Complete Bipartite Subgraphs and Closed Patterns
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.
14732835
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.
5637831
Policy-hiding access control in open environment
In trust management and attribute-based access control systems, access control decisions are based on the attributes (rather than the identity) of the requester: Access is granted if Alice's attributes in her certificates satisfy Bob's access control policy. In this paper, we develop a policy-hiding access control scheme that protects both sensitive attributes and sensitive policies. That is, Bob can decide whether Alice's certified attribute values satisfy Bob's policy, without Bob learning any other information about Alice's attribute values or Alice learning Bob's policy. To enable policy-hiding access control, we introduce the notion of certified input private policy evaluation. Our construction uses Yao's scrambled circuit protocol and two new techniques introduced in this paper. One novel technique is constructing circuits with uniform topology that can compute arbitrary functions in a family. The other technique is committed-integer based oblivious transfer.
338997
A new Mallows distance based metric for comparing clusterings
Despite of the large number of algorithms developed for clustering, the study on comparing clustering results is limited. In this paper, we propose a measure for comparing clustering results to tackle two issues insufficiently addressed or even overlooked by existing methods: (a) taking into account the distance between cluster representatives when assessing the similarity of clustering results; (b) constructing a unified framework for defining a distance based on either hard or soft clustering and ensuring the triangle inequality under the definition. Our measure is derived from a complete and globally optimal matching between clusters in two clustering results. It is shown that the distance is an instance of the Mallows distance---a metric between probability distributions in statistics. As a result, the defined distance inherits desirable properties from the Mallows distance. Experiments show that our clustering distance measure successfully handles cases difficult for other measures.
13270687
Multi-channel Noise Reduction in Noisy Environments
Multi-channel noise reduction has been widely researched to reduce acoustic noise signals and to improve the performance of many speech applications in noisy environments. In this paper, we first introduce the state-of-the-art multi-channel noise reduction methods, especially beamforming based methods, and discuss their performance limitations. Subsequently, we present a multi-channel noise reduction system we are developing that consists of localized noise suppression by microphone array and non-localized noise suppression by post-filtering. Experimental results are also presented to show the benefits of our developed noise reduction system with respect to the traditional algorithms in terms of speech recognition rate. Some suggestions are finally presented for the further research.
10425971
A method to take account of inhomogeneity in mechanical component reliability calculations
This paper proposes a method by which material inhomogeneity may be taken into account in a reliability calculation. The method employs Monte-Carlo simulation; and introduces a material strength index, and a standard deviation of material strength to model the variation in the strength of a component throughout its volume. The method is compared to conventional load-strength interference theory. The results are identical for the case of homogeneous material, but reliability is shown to reduce for the same load as the component volume increases. The case of a tensile bar is used to explore the variation of reliability with component volume.
12799351
Mining risk patterns in medical data
In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.