8110517
FGKA: a Fast Genetic K-means Clustering Algorithm
In this paper, we propose a new clustering algorithm called Fast Genetic K-means Algorithm (FGKA). FGKA is inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty in 1999 but features several improvements over GKA. Our experiments indicate that, while K-means algorithm might converge to a local optimum, both FGKA and GKA always converge to the global optimum eventually but FGKA runs much faster than GKA.
7907707
Fast search in DNA sequence databases using punctuation and indexing
Exact pattern searching in DNA sequence databases has applications in identification of highly conserved regulatory sequences, the design of hybridization probes, and improving performance of approximate homology searching tools such as BLAST and BLAT. We propose a new pattern searching algorithm, Compressed-Punctuated-Boyer-Moore (cp-BM), to enhance exact pattern match searches of DNA sequences. cp-BM encodes two bits to represent each A, T, C, G character (4-character 8 bit (4C8B) compression), plus punctuator characters to indicate unambiguously the encoding frame of the compressed target sequence, thereby solving the misalignment problem in searching patterns with ordinary 4C8B compression. cp-BM searches DNA patterns at least 6 times faster than AGREP for pattern lengths ≥ 128 and between 2-fold and 5-fold faster than d-BM for all pattern lengths. cp-BM's performance is enhanced by punctuator indexing and multiple punctuators, especially for short sequences, yielding greater than 10-fold enhancements compared to d-BM and AGREP. In addition, cp-BM outperformed BLAT for sequences 64 or more bases in length, and was more than three-fold faster for 256 base sequences.
1552056
Pama: a Fast String Matching Algorithm
String matching is a fundamental operation in computer science, and its performance has great impact on many applications including database query, text processing, DNA and protein sequence analysis. In this paper, we propose a fast string matching algorithm, PAMA (PAttern MAtching). The shift rule used by PAMA not only subsumes both the bad character rule and the good suffix rule employed by the well-known Boyer-Moore algorithm, but also employs an additional key observation to enable faster shifting during the string matching process. Theoretically, we prove that from the same alignment, the next shift of PAMA will be at least as much as that of the Boyer-Moore algorithm. Experimentally, we show that PAMA indeed significantly outperforms the original Boyer-Moore algorithm in almost all cases, and outperforms other Boyer-Moore variants such as Tuned-BM, Turbo-BM and Horspool for long patterns (length ≥ 128) or for small alphabets (size < 8).
11276211
PDC: Pattern discovery with confidence in DNA sequences
Pattern discovery in DNA sequences is one of the most challenging tasks in molecular biology and computer science. The main goal of pattern discovery in DNA sequences is to identify sequences of important biological function hidden in the huge amounts of genomic sequences. Several methods and techniques have been proposed and implemented in this field. However, in order to reduce computational time and complexity, most of them either focus on finding short DNA patterns or require explicit specification of pattern lengths in advance. Scientists need to find longer patterns without specifying pattern lengths in advance and still have good performance.In this paper, we propose a pattern discovery algorithm called Pattern Discovery with Confidence (PDC). Based on biological studies, we propose a new measurement system that can identify overrepresented patterns inside DNA sequences. Using this measurement, PDC algorithm can narrow the search space by checking dependency along the pattern, thus extending the pattern as long as possible without the need to restrict or specify the length of a pattern in advance. Experimental tests demonstrate that this approach can find long, interesting patterns within a reasonable computation time.
16579446
Mining Correlation between Motifs and Gene Expression
One of the major challenges in the post-genomic era is to determine all DNA-binding transcription factors (TFs) and their regulatory binding sites (motifs) within the genomes. To discover the relationship between the motifs and changes in gene expression, we propose a new algorithm, co-miner (correlation miner). Correlation rules are generated based on the expression profiles of genes with significant expression change through the time course of gene expression. Thus, we may consider the change in gene expression to be causatively associated with the transcription binding sites in the upstream sequences. In addition, we introduce partition and constraint pushing techniques to improve the performance and demonstrate their effectiveness by our experiments. By applying co-miner to a yeast dataset, the relationships between motifs and gene expression revealed by co-miner are confirmed in the literature.
7682350
Incremental genetic K-means algorithm and its application in gene expression data analysis
BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
7685891
Quantitative comparison of power management algorithms
Dynamic power management saves power by shutting down idle devices. Several management algorithms have been proposed and demonstrated to be effective in certain applications. We quantitatively compare the power saving and performance impact of these algorithms on hard disks of a desktop and notebook computers. This paper has three contributions. First, we build a framework in Windows NT to implement power managers running realistic workloads and directly interacting with users. Second, we define performance degradation that reflects user perception. Finally, we compare power saving and performance of existing algorithms and analyze the difference.
10102267
Parallel Randomized Support Vector Machine
A parallel support vector machine based on randomized sampling technique is proposed in this paper. We modeled a new LP-type problem so that it works for general linear-nonseparable SVM training problems unlike the previous work [2]. A unique priority based sampling mechanism is used so that we can prove an average convergence rate that is so far the fastest bounded convergence rate to the best of our knowledge. The numerical results on synthesized data and a real geometric database show that our algorithm has good scalability.
7140152
Energy Reduction by Workload Adaptation in a Multi-Process Environment
Reducing energy consumption is an important issue in modern computers. Dynamic power management (DPM) has been extensively studied in recent years. One approach for DPM is to adjust workloads, such as clustering or eliminating requests, as a way to trade-off energy consumption and quality of services. Previous studies focus on single processes. However, when multiple concurrently running processes are considered, workload adjustment must be determined based on the interleaving of the processes' requests. When multiple processes share the same hardware component, adjusting one process may not save energy. This paper presents an approach to assign energy responsibility to individual processes based on how they affect power management. The assignment is used to estimate potential energy reduction by adjusting the processes. We use the estimation to guide runtime adaptation of workload behavior. Experiments demonstrate that our approach can save more energy and improve energy efficiency
3148116
Modeling of wave behavior of substrate noise coupling for mixed-signal IC design
A new full-wave method is introduced for substrate noise analysis and simulation. The method is based on solution of the wave equation for the magnetic potential and can be implemented using standard circuit simulators. We compare the new method with the standard quasi-static method for typical substrate profiles and investigate the limits of validity of the quasi-static method.
18145477
Wyner-Ziv Video Coding Based on Set Partitioning in Hierarchical Tree
In this paper, we propose a Wyner-Ziv video coding scheme based on set-partitioning in hierarchical trees (SPIHT) which can utilizing not only the spatial and temporal correlations but also the high-order statistical correlations. Wyner-Ziv theory on source coding with side information is employed as the basic coding principle, which makes the independent encoding and joint decoding become possible. In the proposed scheme, wavelet transform is first used to de-correlate the spatial dependency of a Wyner-Ziv frame. Then the quantized transform coefficients are organized by using magnitude with a set partitioning sorting algorithm. The ordered bit planes are coded using the Wyner-Ziv coding based on turbo codes. At the decoder, side information generated by motion compensated interpolation is used to conditionally decode the Wyner-Ziv frame. Since the high order statistical correlation is used, the proposed algorithm owns advantages over the traditional pixel-domain and transform-domain Wyner-ziv video coding schemes.