12408696
DNACompress: fast and effective DNA sequence compression
While achieving the best compression ratios for DNA sequences, our new DNACompress program significantly improves the running time of all previous DNA compression programs.
12811258
A Parsimony Approach to Genome-Wide Ortholog Assignment
The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics, since many computational methods for solving various biological problems critically rely on bona fide orthologs as input. While it is usually done using sequence similarity search, we recently proposed a new combinatorial approach that combines sequence similarity and genome rearrangement. This paper continues the development of the approach and unites genome rearrangement events and (post-speciation) duplication events in a single framework under the parsimony principle. In this framework, orthologous genes are assumed to correspond to each other in the most parsimonious evolutionary scenario involving both genome rearrangement and (post-speciation) gene duplication. Besides several original algorithmic contributions, the enhanced method allows for the detection of inparalogs. Following this approach, we have implemented a high-throughput system for ortholog assignment on a genome scale, called MSOAR, and applied it to the genomes of human and mouse. As the result will show, MSOAR is able to find 99 more true orthologs than the INPARANOID program did. We have also compared MSOAR with the iterated exemplar algorithm on simulated data and found that MSOAR performed very well in terms of assignment accuracy. These test results indiate that our approach is very promising for genome-wide ortholog assignment.
1529480
A compression algorithm for DNA sequences and its applications in genome comparison
We present a lossless compression algorithm, <italic>Gen-Compress</italic>, for DNA sequences, based on searching for approximate repeats. Our algorithm achieves the best compression ratios for benchmark DNA sequences, comparing to other DNA compression programs [3, 7]. Significantly better compression results show that the approximate repeats are one of the main hidden regularities in DNA sequences.
We then describe a theory of measuring the relatedness between two DNA sequences. We propose to use <italic>d</italic>(<italic>x</italic>, <italic>y</italic>) = 1 — <italic>K</italic>(<italic>x</italic>) - <italic>K</italic>(<italic>x</italic>|<italic>y</italic>)/<italic>K</italic>(<italic>xy</italic> to measure the distance of any two sequences, where <italic>K</italic>  stands for Kolmogorov complexity [5]. Here, <italic>K</italic>(<italic>x</italic>) - <italic>K</italic>(<italic>x</italic>|<italic>y</italic>) is the mutual information shared by <italic>x</italic> and <italic>y</italic>. But mutual information is not a distance, there is no triangle inequality. The distance <italic>d</italic>(<italic>x</italic>, <italic>y</italic>) is symmetric. It also satisfies the triangle inequality, and furthermore, it is universal [4].
It has not escaped our notice that the distance measure we have postulated can be immediately used to construct evolutionary trees from DNA sequences, especially those that cannot be aligned, such as complete genomes. With more and more genomes sequenced, constructing trees from genomes becomes possible [1, 2, 6, 8].  Kolmogorov complexity is not computable. We use <italic>GenCompress</italic> to approximate it. We present strong experimental support for this theory, and demonstrate its applicability by correctly constructing a 16S (18S) rRNA tree, and a whole genome tree for several species of bacteria. Larger scale experiments are underway at the University of Waterloo, with very promising results.
8099146
A new compression scheme for color-quantized images
An efficient compression scheme for color-quantized images based on progressive coding of color information has been developed. Instead of sorting color indexes into a linear list struc- ture, a binary-tree structure of color indexes is proposed. With this structure, the new algorithm can progressively recover an image from two colors to all of the colors contained in the original image, i.e., a lossless recovery is achieved. Experimental results showed that it can efficiently compress images in both lossy and lossless cases. Typically for color-quantized Lena image with 256 colors, the algorithm achieved 0.5 bpp below state-of-the-art lossless com- pression methods while preserving the efficient lossy compression. Such a compression scheme is very attractive to many applications that require the ability of fast browsing or progressive transmis- sion, and if necessary, to exactly recover the original image. Index Terms—Color-quantized images, data compression, index sorting, progressive refinement.
960732
Computing the Assignment of Orthologous Genes via Genome Rearrangement
The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not clearly delineate the evolutionary relationship among genes of the same families. In this paper, we present a new approach to ortholog assignment that takes into account both sequence similarity and evolutionary events at genome level, where orthologous genes are assumed to correspond to each other in the most parsimonious evolving scenario under genome rearrangement. It is then formulated as a problem of computing the signed reversal distance with duplicates between two genomes of interest, for which an efcient heuristic algorithm was given by introducing two new optimization problems, minimum common partition and maximum cycle decomposition. Following this approach, we have implemented a high-throughput system for assigning orthologs on a genome scale, called SOAR, and tested it on both simulated data and real genome sequence data. Compared to a recent ortholog assignment method based entirely on homology search (called INPARANOID), SOAR shows a marginally better performance in terms of sensitivity on the real data set because it was able to identify several correct orthologous pairs that were missed by INPARANOID. The simulation results demonstrate that SOAR in general performs better than the iterated exemplar algorithm in terms of computing the reversal distance and assigning correct orthologs.
1552680
Shared information and program plagiarism detection
A fundamental question in information theory and in computer science is how to measure similarity or the amount of shared information between two sequences. We have proposed a metric, based on Kolmogorov complexity, to answer this question and have proven it to be universal. We apply this metric in measuring the amount of shared information between two computer programs, to enable plagiarism detection. We have designed and implemented a practical system SID (Software Integrity Diagnosis system) that approximates this metric by a heuristic compression algorithm. Experimental results demonstrate that SID has clear advantages over other plagiarism detection systems. SID system server is online at http://software.bioinformatics.uwaterloo.ca/SID/.
38430290
3-NASH is PPAD-Complete
In this paper, we improve a recent result of Daskalakis, Goldberg and Papadimitriou on PPAD-completeness of 4-Nash, showing that 3-Nash is PPAD-complete.
34040250
Connected domination critical graphs
Abstract A dominating set in a graph G is a connected dominating set of G if it induces a connected subgraph of G . The minimum number of vertices in a connected dominating set of G is called the connected domination number of G , and is denoted by γ c ( G ). The purpose of this paper is to initiate an investigation of those graphs which are critical in the following sense: for each υ, μ ϵ V (G) with υ not adjacent to u , γ c ( G + υ u ) c ( G ). Thus, G is k -γ c -critical if γ c ( G ) = k and for each edge e ∉ E ( G ), γ c ( G + e ) k . First we discuss whether some particular classes of graphs are γ c -critical. Then 2-γ c -critical graphs are characterized. Finally, the properties of the 3-γ c -critical graphs are studied.
33892015
A Multiple-Criteria Quadratic Programming Approach to Network Intrusion Detection
The early and reliable detection and deterrence of malicious attacks, both from external and internal sources are a crucial issue for today’s e-business. There are various methods available today for intrusion detection; however, every method has its limitations and new approaches should still be explored. The objectives of this study are twofold: one is to discuss the formulation of Multiple Criteria Quadratic Programming (MCQP) approach, and to investigate the applicability of the quadratic classification method to the intrusion detection problem. The demonstration of successful Multiple Criteria Quadratic Programming application in intrusion detection can add another option to network security toolbox. The classification results are examined by cross-validation and improved by an ensemble method. The results demonstrated that MCQP is excellent and stable. Furthermore, the outcome of MCQP can be improved by the ensemble method.
12374753
Efficient Method for Geometric Attribute Estimation
A new method is proposed to estimate the discrete geometric attributes, such as normal vector and curvature. The method contains two steps: points sampling and 4D Shepard interpolation. In the first step, we extract points from point cloud and estimate the attribute of the sampled points. During the second step, we interpolate a global 4D Shepard surface over these sampled points and compute the attribute of each point in point cloud. This method is very effective for using a new global surface model. It benefits many applications such as segmentation, feature extraction, data preprocessing and etc. Application examples are given to verify the proposed method.
11357832
A latent semantic indexing based method for solving multiple instance learning problem in region-based image retrieval
Relevance feedback (RF) is a widely used technique in incorporating user's knowledge with the learning process for content-based image retrieval (CBIR). As a supervised learning technique, it has been shown to significantly increase the retrieval accuracy. However, as a CBIR system continues to receive user queries and user feedbacks, the information of user preferences across query sessions are often lost at the end of search, thus requiring the feedback process to be restarted for each new query. A few works targeting long-term learning have been done in general CBIR domain to alleviate this problem. However, none of them address the needs and long-term similarity learning techniques for region-based image retrieval. This paper proposes a latent semantic indexing (LSI) based method to utilize users' relevance feedback information. The proposed region-based image retrieval system is constructed on a multiple instance learning (MIL) framework with one-class support vector machine (SVM) as its core. Experiments show that the proposed method can better utilize users' feedbacks of previous sessions, thus improving the performance of the learning algorithm (one-class SVM).