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Dive into the research topics where Ming Ying Leung is active.

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Featured researches published by Ming Ying Leung.


international parallel and distributed processing symposium | 2002

Load balancing in distributed systems: an approach using cooperative games

Daniel Grosu; Anthony T. Chronopoulos; Ming Ying Leung

In this paper we formulate the static load balancing problem in single class job distributed systems as a cooperative game among computers. It is shown that the Nash Bargaining Solution (NBS) provides a Pareto optimal allocation which is also fair to all jobs. We propose a cooperative load balancing game and present the structure of the NBS For this game an algorithm for computing NBS is derived. We show that the fairness index is, always 1 using NBS which means that the allocation is fair to all jobs. Finally, the performance of our cooperative load balancing scheme is compared with that of other existing schemes.


Journal of Molecular Biology | 1991

An efficient algorithm for identifying matches with errors in multiple long molecular sequences

Ming Ying Leung; B. Edwin Blaisdell; Christopher B. Burge; Samuel Karlin

An efficient algorithm is described for finding matches, repeats and other word relations, allowing for errors, in large data sets of long molecular sequences. The algorithm entails hashing on fixed-size words in conjunction with the use of a linked list connecting all occurrences of the same word. The average memory and run time requirement both increase almost linearly with the total sequence length. Some results of the programs performance on a database of Escherichia coli DNA sequences are presented.


Nucleic Acids Research | 2009

PseudoBase++: an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots.

Abel Licon; Roberto Araiza; David Mireles; F. H D van Batenburg; Alexander P. Gultyaev; Ming Ying Leung

Pseudoknots have been recognized to be an important type of RNA secondary structures responsible for many biological functions. PseudoBase, a widely used database of pseudoknot secondary structures developed at Leiden University, contains over 250 records of pseudoknots obtained in the past 25 years through crystallography, NMR, mutational experiments and sequence comparisons. To promptly address the growing analysis requests of the researchers on RNA structures and bring together information from multiple sources across the Internet to a single platform, we designed and implemented PseudoBase++, an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots. PseudoBase++ (http://pseudobaseplusplus.utep.edu) maps the PseudoBase dataset into a searchable relational database including additional functionalities such as pseudoknot type. PseudoBase++ links each pseudoknot in PseudoBase to the GenBank record of the corresponding nucleotide sequence and allows scientists to automatically visualize RNA secondary structures with PseudoViewer. It also includes the capabilities of fine-grained reference searching and collecting new pseudoknot information.


Journal of Applied Probability | 1984

SELF-ORGANIZING FILES WITH DEPENDENT ACCESSES

Kin Lam; Ming Ying Leung; Man-Keung Siu

We analyze certain self-organizing filing techniques when accesses are assumed to be dependent on each other. The stream of requests for accessing records in a file is modelled as a Markov chain. A general framework is introduced to obtain the asymptotic search cost of a memory-free selforganizing heuristic. The move-to-front heuristic is studied in detail. A formula for the asymptotic search cost, which generalizes that in the case of independent accesses, is obtained. Numerical examples on the performance of the transposition heuristic are provided, and compared with that of the move-to-front


Journal of Computational Biology | 2005

Nonrandom Clusters of Palindromes in Herpesvirus Genomes

Ming Ying Leung; Kwok Pui Choi; Aihua Xia; Louis H. Y. Chen

Palindromes are symmetrical words of DNA in the sense that they read exactly the same as their reverse complementary sequences. Representing the occurrences of palindromes in a DNA molecule as points on the unit interval, the scan statistics can be used to identify regions of unusually high concentration of palindromes. These regions have been associated with the replication origins on a few herpesviruses in previous studies. However, the use of scan statistics requires the assumption that the points representing the palindromes are independently and uniformly distributed on the unit interval. In this paper, we provide a mathematical basis for this assumption by showing that in randomly generated DNA sequences, the occurrences of palindromes can be approximated by a Poisson process. An easily computable upper bound on the Wasserstein distance between the palindrome process and the Poisson process is obtained. This bound is then used as a guide to choose an optimal palindrome length in the analysis of a collection of 16 herpesvirus genomes. Regions harboring significant palindrome clusters are identified and compared to known locations of replication origins. This analysis brings out a few interesting extensions of the scan statistics that can help formulate an algorithm for more accurate prediction of replication origins.


BMC Structural Biology | 2013

Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce

Boyu Zhang; Daniel T. Yehdego; Kyle L. Johnson; Ming Ying Leung

BackgroundRibonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment.ResultsOn average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance.ConclusionsBy using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone.


European Journal of Immunology | 2010

Generation of robust CD8+ T-cell responses against subdominant epitopes in conserved regions of HIV-1 by repertoire mining with mimotopes.

Keri L. Schaubert; David A. Price; Janelle R. Salkowitz; Andrew K. Sewell; John Sidney; Tedi E. Asher; Sylvie E. Blondelle; Sharon Adams; Francesco M. Marincola; Aviva Joseph; Alessandro Sette; Velpandi Ayyavoo; Walter J. Storkus; Ming Ying Leung; Hwee L. Ng; Otto O. Yang; Harris Goldstein; Darcy B. Wilson; June Kan-Mitchell

HLA‐A*0201‐restricted virus‐specific CD8+ CTL do not appear to control HIV effectively in vivo. To enhance the immunogenicity of a highly conserved subdominant epitope, TV9 (TLNAWVKVV, p24 Gag19–27), mimotopes were designed by screening a large combinatorial nonapeptide library with TV9‐specific CTL primed in vitro from healthy donors. A mimic peptide with a low binding affinity to HLA‐A*0201, TV9p6 (KINAWIKVV), was studied further. Parallel cultures of in vitro‐primed CTL showed that TV9p6 consistently activated cross‐reactive and equally functional CTL as measured by cytotoxicity, cytokine production and suppression of HIV replication in vitro. Comparison of TCRB gene usage between CTL primed from the same donors with TV9 or TV9p6 revealed a degree of clonal overlap in some cases and an example of a conserved TCRB sequence encoded distinctly at the nucleotide level between individuals (a “public” TCR); however, in the main, distinct clonotypes were recruited by each peptide antigen. These findings indicate that mimotopes can mobilize functional cross‐reactive clonotypes that are less readily recruited from the naïve T‐cell pool by the corresponding WT epitope. Mimotope‐induced repertoire diversification could potentially override subdominance under certain circumstances and enhance vaccine‐induced responses to conserved but poorly immunogenic determinants within the HIV proteome.


BMC Bioinformatics | 2007

AT excursion: a new approach to predict replication origins in viral genomes by locating AT-rich regions.

David S H Chew; Ming Ying Leung; Kwok Pui Choi

BackgroundReplication origins are considered important sites for understanding the molecular mechanisms involved in DNA replication. Many computational methods have been developed for predicting their locations in archaeal, bacterial and eukaryotic genomes. However, a prediction method designed for a particular kind of genomes might not work well for another. In this paper, we propose the AT excursion method, which is a score-based approach, to quantify local AT abundance in genomic sequences and use the identified high scoring segments for predicting replication origins. This method has the advantages of requiring no preset window size and having rigorous criteria to evaluate statistical significance of high scoring segments.ResultsWe have evaluated the AT excursion method by checking its predictions against known replication origins in herpesviruses and comparing its performance with an existing base weighted score method (BWS1). Out of 43 known origins, 39 are predicted by either one or the other method and 26 origins are predicted by both. The excursion method identifies six origins not predicted by BWS1, showing that the AT excursion method is a valuable complement to BWS1. We have also applied the AT excursion method to two other families of double stranded DNA viruses, the poxviruses and iridoviruses, of which very few replication origins are documented in the public domain. The prediction results are made available as supplementary materials at [1]. Preliminary investigation shows that the proposed method works well on some larger genomes too.ConclusionThe AT excursion method will be a useful computational tool for identifying replication origins in a variety of genomic sequences.


parallel computing | 2008

RNAVLab: A virtual laboratory for studying RNA secondary structures based on grid computing technology

Ming Ying Leung; Thamar Solorio; Abel Licon; David Mireles; Roberto Araiza; Kyle L. Johnson

Abstract As ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation, their secondary structures have been the focus of many recent studies. Despite the computing power of supercomputers, computationally predicting secondary structures with thermodynamic methods is still not feasible when the RNA molecules have long nucleotide sequences and include complex motifs such as pseudoknots. This paper presents RNAVLab (RNA Virtual Laboratory), a virtual laboratory for studying RNA secondary structures including pseudoknots that allows scientists to address this challenge. Two important case studies show the versatility and functionalities of RNAVLab. The first study quantifies its capability to rebuild longer secondary structures from motifs found in systematically sampled nucleotide segments. The extensive sampling and predictions are made feasible in a short turnaround time because of the grid technology used. The second study shows how RNAVLab allows scientists to study the viral RNA genome replication mechanisms used by members of the virus family Nodaviridae.


international parallel and distributed processing symposium | 2008

On the Effectiveness of Rebuilding RNA Secondary Structures from Sequence Chunks

Thamar Solorio; Abel Licon; David Mireles; Ming Ying Leung

Despite the computing power of emerging technologies, predicting long RNA secondary structures with thermodynamics-based methods is still infeasible, especially if the structures include complex motifs such as pseudoknots. This paper presents preliminary results on rebuilding RNA secondary structures by an extensive and systematic sampling of nucleotide chunks. The rebuilding approach merges the significant motifs found in the secondary structures of the single chunks. The extensive sampling and prediction of nucleotide chunks are supported by grid technology as part of the RNAVLab functionality. Significant motifs are identified in the chunk secondary structures and merged in a single structure based on their recurrences and other statistical insights. A critical analysis of the strengths, weaknesses, and future developments of our method is presented.

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Kyle L. Johnson

University of Texas at El Paso

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Abel Licon

University of Delaware

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Boyu Zhang

University of Delaware

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Daniel T. Yehdego

University of Texas at El Paso

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Olac Fuentes

University of Texas at El Paso

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Roberto Araiza

University of Texas at El Paso

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Kwok Pui Choi

National University of Singapore

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David Mireles

University of Texas at El Paso

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