5,460 research outputs found
THE PM221 INTERCONNECTION NETWORK
No full textThis work was supported in part by The Ohio State University Seed Grant 221630
An engineering model of the masking for the noise-robust speech recognition
No full textThe masking effect of human hearing is modeled by lateral and unilateral inhibition, and tested for isolated word recognition tasks. Frequency masking suppresses unwanted signals close to the dominant signal of interest in frequency domain, and the weak signals following dominant ones in the time are suppressed by temporal forward masking. The masking effect filters out unimportant signals, which may improve the performance of speech recognition systems. With the parameters derived from the psychological observations, proposed model shows good analogy to psychoacoustic masking effects as well as superior recognition performance. (C) 2003 Elsevier Science B.V. All rights reserved.This research was supported by Korean Ministry of Science and Technology as Brain
Neuroinformatics Research Program
Evaluation of the performance of clustering algorithms in kernel-induced feature space
No full textBy using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However. few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.This work was supported by the Korean Systems Biology Research Grant (M1-0309-02-0002) from the Ministry of Science and Technology. We would like to thank Chung
Moon Soul Center for BioInformation and BioElectronics and the IBM SUR program for providing research and computing facilities
Characterization of an endoxylanase produced by an isolated strain of Bacillus sp
No full textMicroorganisms producing xylanase were screened for the enzymatic production of xylo-oligosaccharides from xylan. One of the bacteria isolated fi om compost produced an endoxylanase extracellularly. The bacterium was identified as Bacillus sp. according to its taxonomic characteristics examined. Xylanase production reached upto 5 U/ml after 22 h of culture in LB medium at 30 degrees C. The xylanase was purified by ammonium sulfate precipitation and gel filtration. The molecular weight of the xylanase was estimated to be 20,400 by SDS-PAGE. Optimal temperature and pH for the xylanase activity was 60 degrees C and 6.5, respectively. The enzyme was stable at temperatures upto 40 degrees C and pH values from 4 to 10. The xylanase was completely inhibited by the addition of 2 mM mercury ion. Apparent K-m and V-max values for oat spelt xylan were 9.2 mg/ml and 1954 U/mg protein, respectively. For birchwood xylan, the values were 6.3 mg/ml and 1009 U/mg protein, The predominant products of the xylan hydrolysis were xylobiose, xylotriose and xylotetraose, indicating that the enzyme is an endoxylanase. Upto 85% of the initially added enzyme (2 U/ml) was bound to 50 mg/ml of the insoluble fraction of oat spelt xylan after incubation at 30 degrees C for 30 min
Towards clustering of incomplete microarray data without the use of imputation
No full textMotivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. Results: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data
Identification of temporal association rules from time-series microarray data set
No full textBackground: One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A up arrow, gene B down arrow] -> (7 min) [gene C up arrow], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.
Results: In the parameter fitting phase of TARM, the fitted parameter set [threshold = +/- 0.8, support >= 3 transactions, confidence >= 90%] with the best precision score for KEGG cell cycle pathway has been chosen for rule mining phase. With the fitted parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes, which are pre-identified cell cycle relevant genes. From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.
Conclusion: In this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network. Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators
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