2115385
Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications
This paper describes several case studies concerning protein function inference from its structure using our novel approach described in the accompanying paper. This approach employs family-specific motifs, i.e. three-dimensional amino acid packing patterns that are statistically prevalent within a protein family. For our case studies we have selected families from the SCOP and EC classifications and analyzed the discriminating power of the motifs in depth. We have devised several benchmarks to compare motifs mined from unweighted topological graph representations of protein structures with those from distance-labeled (weighted) representations, demonstrating the superiority of the latter for function inference in most families. We have tested the robustness of our motif library by inferring the function of new members added to SCOP families, and discriminating between several families that are structurally similar but functionally divergent. Furthermore we have applied our method to predict function for several proteins characterized in structural genomics projects, including orphan structures, and we discuss several selected predictions in depth. Some of our predictions have been corroborated by other computational methods, and some have been validated by independent experimental studies, validating our approach for protein function inference from structure.
5788516
Efficient genome ancestry inference in complex pedigrees with inbreeding
Motivation: High-density SNP data of model animal resources provides opportunities for fine-resolution genetic variation studies. These genetic resources are generated through a variety of breeding schemes that involve multiple generations of matings derived from a set of founder animals. In this article, we investigate the problem of inferring the most probable ancestry of resulting genotypes, given a set of founder genotypes. Due to computational difficulty, existing methods either handle only small pedigree data or disregard the pedigree structure. However, large pedigrees of model animal resources often contain repetitive substructures that can be utilized in accelerating computation. Results: We present an accurate and efficient method that can accept complex pedigrees with inbreeding in inferring genome ancestry. Inbreeding is a commonly used process in generating genetically diverse and reproducible animals. It is often carried out for many generations and can account for most of the computational complexity in real-world model animal pedigrees. Our method builds a hidden Markov model that derives the ancestry probabilities through inbreeding process without explicit modeling in every generation. The ancestry inference is accurate and fast, independent of the number of generations, for model animal resources such as the Collaborative Cross (CC). Experiments on both simulated and real CC data demonstrate that our method offers comparable accuracy to those methods that build an explicit model of the entire pedigree, but much better scalability with respect to the pedigree size. Contact: weiwang@cs.unc.edu
2548187
Mining asynchronous periodic patterns in time series data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min rep and max dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space
883049
Functional Neighbors: Inferring Relationships between Non-Homologous Protein Families Using Family-Specific Packing Motifs
We describe a new approach for inferring the functional relationships between non-homologous protein families by looking at statistical enrichment of alternative function predictions in classification hierarchies such as Gene Ontology (GO) and Structural Classification of Proteins (SCOP). Protein structures are represented by robust graphs, and the Fast Frequent Subgraph Mining algorithm is applied to protein families to generate sets of family-specific packing motifs, i.e. amino acid residue packing patterns shared by most family members but infrequent in other proteins. The function of a protein is inferred by identifying in it motifs characteristic of a known family. We employ these family-specific motifs to elucidate functional relationships between families in the GO and SCOP hierarchies. Specifically, we postulate that two families are functionally related if one family is statistically enriched by motifs characteristic of another family, i.e. if the number of proteins in a family containing a motif from another family is greater than expected by chance. This function inference method can help annotate proteins of unknown function, establish functional neighbors of existing families, and help specify alternate functions for known proteins.
917867
Graph Database Indexing Using Structured Graph Decomposition
We introduce a novel method of indexing graph databases in order to facilitate subgraph isomorphism and similarity queries. The index is comprised of two major data structures. The primary structure is a directed acyclic graph which contains a node for each of the unique, induced subgraphs of the database graphs. The secondary structure is a hash table which cross-indexes each subgraph for fast isomorphic lookup. In order to create a hash key independent of isomorphism, we utilize a code-based canonical representation of adjacency matrices, which we have further refined to improve computation speed. We validate the concept by demonstrating its effectiveness in answering queries for two practical datasets. Our experiments show that for subgraph isomorphism queries, our method outperforms existing methods by more than an order of magnitude.
62615149
Mining Asynchronous Periodic Patterns in Time Series Data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min/spl I.bar/rep and max/spl I.bar/dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space efficiency.
11683146
Collaborative Image Transmissions Based on Region and Path Diversity in Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor networks (WSN). The large size image transmissions cause bottlenecks in WSN due to the limited energy resources and network capacity. In this paper, we propose a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on transmission path diversities, which achieves minimal energy consumption, balanced sensor lifetime and required image quality. This optimization scheme not only allows each image sensor to transmit optimal fractions of the overlapped images through appropriate transmission paths in energy-efficient way, but also provides unequal protection on the overlap image regions through path selections and resource allocations to achieve good transmission image quality. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the network lifetime extension, image distortion reduction, and energy efficiency.
18635626
Storing Normalized XML Documents in Normalized Relations
This paper studies the XML storage in relations. Unlike traditional techniques, it considers the semantics expressed by functional dependencies. We propose an algorithm for mapping DTD to relational schema, which preserves not only the content and structure but also the semantics of original XML documents. To tackle the problem of constraints expression, we introduce a way to define functional dependencies and normalization for DTD. In a normalized DTD, every constraints expressed by functional dependencies can be concluded to keys. So we use the key definitions for XML as the foundation for relation generation, and maintain the keys in relations. After investigating the relationship between functional dependencies in XML documents with the corresponding ones in relations, we further prove that, if the original DTD is normalized, the generated relations will be in BCNF. So our method keeps the good properties of normalized DTD, and can fully leverage the relational technology
14692373
SlidingWindow based Multi-Join Algorithms over Distributed Data Streams
This paper focuses on multi-way sliding window join (SWJoin) processing over distributed data streams. A novel Join algorithm is proposed based on two distributed data stream transfer models. To reduce the communication cost and lighten the workload on the central processor node, the algorithm filters out tuples that can’t contribute to multiway SWJoin results by transforming the join conditions of SWJoin into filtering conditions during data stream transfer. Furthermore, the algorithm guarantees that all necessary data for generating exact multi-way SWJoin results can be transmitted to the central processor node.
15939515
GraphMiner: a structural pattern-mining system for large disk-based graph databases and its applications
Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system--- GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.
1043853
Scalable mining of large disk-based graph databases
Mining frequent structural patterns from graph databases is an interesting problem with broad applications. Most of the previous studies focus on pruning unfruitful search subspaces effectively, but few of them address the mining on large, disk-based databases. As many graph databases in applications cannot be held into main memory, scalable mining of large, disk-based graph databases remains a challenging problem. In this paper, we develop an effective index structure, ADI (for <u>ad</u>jacency <u>i</u>ndex), to support mining various graph patterns over large databases that cannot be held into main memory. The index is simple and efficient to build. Moreover, the new index structure can be easily adopted in various existing graph pattern mining algorithms. As an example, we adapt the well-known gSpan algorithm by using the ADI structure. The experimental results show that the new index structure enables the scalable graph pattern mining over large databases. In one set of the experiments, the new disk-based method can mine graph databases with one million graphs, while the original gSpan algorithm can only handle databases of up to 300 thousand graphs. Moreover, our new method is faster than gSpan when both can run in main memory.