40467896
Mining the Database of Transcription Binding Sites
In this paper, we study the problems of motif discovery and gene regulation. First, although the sliding window technique based on profiles or consensus sequences is a standard method for discovering motifs in the genomes with prior knowledge of transcription binding sites in orthologous genes from related organisms, it usually has high computational costs. In this paper, we propose an efficient approximation method employing randomized algorithms to identify motifs. The approximation method can be easily combined with the sliding-window technique for efficient and accurate motif discovery. Second, we mine frequent motif combinations and sequential motif patterns to investigate the regulatory relationships between motifs and provide a better understanding of gene expression, regulation, and transcription
1750917
A general model for clustering binary data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This paper studies the problem of clustering binary data. This is the case for market basket datasets where the transactions contain items and for document datasets where the documents contain "bag of words". The contribution of the paper is three-fold. First a general binary data clustering model is presented. The model treats the data and features equally, based on their symmetric association relations, and explicitly describes the data assignments as well as feature assignments. We characterize several variations with different optimization procedures for the general model. Second, we also establish the connections between our clustering model with other existing clustering methods. Third, we also discuss the problem for determining the number of clusters for binary clustering. Experimental results show the effectiveness of the proposed clustering model.
9813108
CoFD : An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.
8864863
Integrating Features from Different Sources for Music Information Retrieval
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
206784011
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them. Experiments are also conducted to empirically evaluate and compare various factorization methods. In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering. We expect our study would provide good insights on matrix factorization research for clustering.
14819927
Gene functional classification by semi-supervised learning from heterogeneous data
Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semisupervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semisupervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.
6096323
Quantifying instruction criticality for shared memory multiprocessors
Recent research on processor microarchitecture suggests using instruction criticality as a metric to guide hardware control policies. Fields et al. [3, 4] have proposed a directed acyclic graph (DAG) model for characterizing program microexecutions on uniprocessors. Under such a model, critical path analysis can be applied and instructions' slack values can be used to quantify instruction criticality. In this paper, we extend the uniprocessor DAG model to characterize parallel program executions on shared memory multiprocessor systems. We describe how critical path analysis can be applied, at a fine grain, in a multiprocessor system running both finite and continuous workloads. We provide detailed evaluations for various aspects of multiprocessor executions under the DAG model. To enable efficient offline critical path analysis, we propose a novel graph reduction technique that reduces a DAG to an equivalent but significantly smaller DAG.
2911701
Workload characterization of bioinformatics applications
The exponential growth in the amount of genomic information has spurred growing interest in large scale analysis of genetic data. Bioinformatics applications represent the increasingly important workloads. However, very few results on the behavior of these applications running on the state-of-the-art microprocessor and systems have been published. This paper proposes a suite of widely used bioinformatics applications and studies the execution characteristics of these benchmarks on a representative architecture-the Intel Pentium 4. To understand the impacts and implications of bioinformatics workloads on the microprocessor designs, we contrast the characteristics of bioinformatics workloads and the widely used SPEC 2000 integer benchmarks.
14215995
Spin detection hardware for improved management of multithreaded systems
Spinning is a synchronization mechanism commonly used in applications and operating systems. Excessive spinning, however, often indicates performance or correctness (e.g., livelock) problems. Detecting if applications and operating systems are spinning is essential for achieving high performance, especially in consolidated servers running virtual machines. Prior research has used source or binary instrumentation to detect spinning. However, these approaches place a significant burden on programmers and may even be infeasible in certain situations. In this paper, we propose efficient hardware to detect spinning in unmodified applications and operating systems. Based on this hardware, we develop 1) scheduling and power policies that adaptively manage resources for spinning threads, 2) system support that helps detect when a multithreaded program is livelocked, and 3) hardware performance counters that accurately reflect system performance. Using full-system simulation with SPEC OMP, SPLASH-2, and Wisconsin commercial workloads, we demonstrate that our mechanisms effectively improve the management of multithreaded systems
14562465
Bioinformatics on Embedded Systems: A Case Study of Computational Biology Applications on VLIW Architecture
Bioinformatics applications represent the increasingly important workloads. Their characteristics and implications on the underlying hardware design, however, are largely unknown. Currently, biological data processing ubiquitously relies on the high-end systems equipped with expensive, general-purpose processors. The future generation of bioinformatics requires the more flexible and cost-effective computing platforms to meet its rapidly growing market. The programmable, application-specific embedded systems appear to be an attractive solution in terms of easy of programming, design cost, power, portability and time-to-market. The first step towards such systems is to characterize bioinformatics applications on the target architecture. Such studies can help in understanding the design issues and the trade-offs in specializing hardware and software systems to meet the needs of bioinformatics market. This paper evaluates several representative bioinformatics tools on the VLIW based embedded systems. We investigate the basic characteristics of the benchmarks, impact of function units, the efficiency of VLIW execution, cache behavior and the impact of compiler optimizations. The architectural implications observed from this study can be applied to the design optimizations. To the best of our knowledge, this is one of the first such studies that have ever been attempted.
3016046
Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation