James Zijun Wang

A new method to measure the semantic similarity of GO terms

MOTIVATION Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. RESULTS To address this critical need, we proposed a novel method to encode a GO terms semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. AVAILABILITY The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. SUPPLEMENTARY INFORMATION http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.

Exploring local community structures in large networks

In this paper, we extend the concept of degree from single vertex to sub-graph, and present a formal definition of module/community in a network based on this extension. A new locally optimized algorithm is designed to find the module for a given source vertex in a network. Our analysis shows that the complexity of this algorithm is O(K2d) where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. Based on this algorithm, we implement a JAVA tool, MoNet, for exploring local community structures in large networks. Using this tool to analyze a co-purchase network from Amazon shows that there are local community structures in this network. Further analyses on these local community structures demonstrate that media items are much easier to form compact local modules than book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store is more efficient than recommending books

Exploring Local Community Structures in Large Networks

In this paper, we extend the concept of degree from single vertex to sub-graph, and present a formal definition of module/community in a network based on this extension. A new locally optimized algorithm is designed to find the module for a given source vertex in a network. Our analysis shows that the complexity of this algorithm is O(K2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. Based on this algorithm, we implement a JAVA tool, MoNet, for exploring local community structures in large networks. Using this tool to analyze a co-purchase network from Amazon shows that there are local community structures in this network. Further analyses on these local community structures demonstrate that media items are much easier to form compact local modules than book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store will be more efficient than recommending books.

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright

Earthworm: a network memory management technique for large-scale distributed multimedia applications

The two main operating constraints of todays multimedia servers are the I/O bandwidth and communication bandwidth limitations. Both of these problems are addressed in this paper using a novel technique called Earthworm. In this scheme, the network memory is used as a huge cache for buffering multimedia data. Dramatic reduction in the demand on the I/O bandwidth, therefore, can be achieved. This scheme also chains display stations to allow them to forward video streams. This strategy eliminates the congestion at the communication port of the server. Removing this bottleneck allows our technique to operate on the vast aggregate bandwidth of the WAN rather than being constrained by the very limited local bandwidth available to the server. A unique feature of the Earthworm approach is that every display station using the server attempts to make some contribution to the caching space and communication bandwidth. The arrival of a new request, therefore, can be seen as a contributor, rather than just a burden to the server. This characteristic ensures the scalability of our design to support very large multimedia applications.

Concept Forest: A New Ontology-assisted Text Document Similarity Measurement Method

Although using ontologies to assist information retrieval and text document processing has recently attracted more and more attention, existing ontologybased approaches have not shown advantages over the traditional keywords-based Latent Semantic Indexing (LSI) method. This paper proposes an algorithm to extract a concept forest (CF) from a document with the assistance of a natural language ontology, the WordNet lexical database. Using concept forests to represent the semantics of text documents, the semantic similarities of these documents are then measured as the commonalities of their concept forests. Performance studies of text document clustering based on different document similarity measurement methods show that the CF-based similarity measurement is an effective alternative to the existing keywords-based methods. In particular, this CFbased approach has obvious advantages over the existing keywords-based methods, including LSI, in processing short text documents or in P2P or live news environments where it is impractical to collect the entire document corpus for analysis.Although using ontologies to assist information retrieval and text document processing has recently attracted more and more attention, existing ontologybased approaches have not shown advantages over the traditional keywords-based Latent Semantic Indexing (LSI) method. This paper proposes an algorithm to extract a concept forest (CF) from a document with the assistance of a natural language ontology, the WordNet lexical database. Using concept forests to represent the semantics of text documents, the semantic similarities of these documents are then measured as the commonalities of their concept forests. Performance studies of text document clustering based on different document similarity measurement methods show that the CF-based similarity measurement is an effective alternative to the existing keywords-based methods. In particular, this CFbased approach has obvious advantages over the existing keywords-based methods, including LSI, in processing short text documents or in P2P or live news environments where it is impractical to collect the entire document corpus for analysis.

A framework for supporting previewing and VCR operations in a low bandwidth environment

We propose a novel delivery mechanism called 2-Phase Service Model to deliver video data to home users connected to the Internet through a low-bandwidth device such as a modem. In our scheme, non-adjacent fragments of the requested video file are first downloaded to the client during Initialization Phase. The missing fragments are transmitted to the client as the video is being played out, using a novel pipelining technique. This scheme offers several benefits as follows. First, it allows the user to perform a quick preview through the video with minimal delay. Second, it naturally supports VCR functionality with almost no delay as demonstrated by the simulation results shown in the paper. Finally, our mathematical analysis shows that despite the desirable features it offers, 2-Phase Service Model does not incur any more initialization delay than-that of the conventional pipelining technique.

Fragmental Proxy Caching for Streaming Multimedia Objects

In this paper, a fragmental proxy-caching scheme that efficiently manages the streaming multimedia data in proxy cache is proposed to improve the quality of streaming multimedia services. The novel data-fragmentation method in this scheme not only provides finer granularity caching units to allow more effective cache replacement, but also offers a unique and natural way of handling the interactive VCR functions in the proxy-caching environment. Furthermore, a cache-replacement scheme, based on user request arrival rates for different multimedia objects and the playback rates of these objects, is proposed to address the drawbacks in existing cache-replacement schemes, most of which consider only the user access frequencies in their cache-replacement decisions. In this cache-replacement scheme, a sliding history window is employed to monitor the dynamic user request arrivals, and a tunable-victimization procedure is used to provide an excellent method of managing the cached multimedia data in accordance with different quality-of-service requirements of the streaming multimedia applications. Performance studies demonstrate that the fragmental proxy-caching scheme significantly outperforms other caching schemes, in terms of byte-hit ratio and the number of delayed starts and can be tuned to either maximize the byte-hit ratio or minimize the number of delayed starts

Measure the Semantic Similarity of GO Terms Using Aggregate Information Content

The rapid development of Gene Ontology (GO) and huge amount of biomedical data annotated by GO terms necessitate computation of semantic similarity of GO terms and, in turn, measurement of functional similarity of genes based on their annotations. In this paper we propose a novel and efficient method to measure the semantic similarity of GO terms. The proposed method addresses the limitations in existing GO term similarity measurement techniques; it computes the semantic content of a GO term by considering the information content of all of its ancestor terms in the graph. The aggregate information content (AIC) of all ancestor terms of a GO term implicitly reflects the GO terms location in the GO graph and also represents how human beings use this GO term and all its ancestor terms to annotate genes. We show that semantic similarity of GO terms obtained by our method closely matches the human perception. Extensive experimental studies show that this novel method also outperforms all existing methods in terms of the correlation with gene expression data. We have developed Web services for measuring semantic similarity of GO terms and functional similarity of genes using the proposed AIC method and other popular methods. These Web services are available at http://bioinformatics.clemson.edu/G-SESAME.

Data allocation algorithms for distributed video servers

In this paper, We discuss the server level data allocation problems in the distributed Video-on-Demand systems. We proposed two data allocation algorithms, Bandwidth Weighted Partition (BWP) algorithm and Popularity Based (PB) algorithm, based on the bandwidth and storage capacity limits of the distributed multimedia servers. We compare those two algorithms with the traditional Round Robin (RR) algorithm. The analysis and simulation studies show that PB algorithm is a simple and practical video data allocation algorithm for the distributed video servers. It provides near optimal system performance in any system condition.