207738295
Kernel Nearest-Neighbor Algorithm
The ‘kernel approach’ has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. It offers an alternative solution to increase the computational power of linear learning machines by mapping data into a high dimensional feature space. This ‘approach’ is extended to the well-known nearest-neighbor algorithm in this paper. It can be realized by substitution of a kernel distance metric for the original one in Hilbert space, and the corresponding algorithm is called kernel nearest-neighbor algorithm. Three data sets, an artificial data set, BUPA liver disorders database and USPS database, were used for testing. Kernel nearest-neighbor algorithm was compared with conventional nearest-neighbor algorithm and SVM Experiments show that kernel nearest-neighbor algorithm is more powerful than conventional nearest-neighbor algorithm, and it can compete with SVM.
17528755
Significance of Gene Ranking for Classification of Microarray Samples
Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods
14773046
Predicting methylation status of CpG islands in the human brain
MOTIVATION
Over 50% of human genes contain CpG islands in their 5'-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands.


RESULTS
Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database.


AVAILABILITY
An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html


CONTACT
mzhang@cshl.edu


SUPPLEMENTARY INFORMATION
Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.
15047459
Disjoint Triangles of a Cubic Line Graph
Abstract.In this paper, we prove that a cubic line graph G on n vertices rather than the complete graph K4 has vertex-disjoint triangles and the vertex independence number . Moreover, the equitable chromatic number, acyclic chromatic number and bipartite density of G are respectively.
18526893
Scaling and parallelizing a scientific feature mining application using a cluster middleware
Summary form only given. As scientific simulations are generating large amounts of data, analyzing this data to gain insights into scientific phenomenon is increasingly becoming a challenge. We present a case study on the use of a cluster middleware for rapidly creating a scalable and parallel implementation of a scientific data analysis application. Using FREERIDE (framework for rapid implementation of data mining engines), we parallelize as well as scale to disk-resident datasets a feature extraction algorithm. We have developed a parallel algorithm for this problem which matches the communication and computation structure supported by the FREERIDE system. The main observations from our experimental results are as follows: 1) the overhead of using the middleware is quite small in most cases, 2) there is an overhead associated with breaking the datasets into more partitions or chunks, and 3) if the dataset is partitioned into the same number of chunks, the execution time stays proportional to the size of the dataset and inversely proportional to the number of nodes, i.e. the overhead of communication or reading disk-resident datasets is very small.
8220837
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its univariate counterpart. The lower level of interest in multivariate kernel density estimation is mainly due to the increased difficulty in deriving an optimal data-driven bandwidth as the dimension of the data increases. We provide Markov chain Monte Carlo (MCMC) algorithms for estimating optimal bandwidth matrices for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters whose posterior density can be obtained through the likelihood cross-validation criterion. Numerical studies for bivariate data show that the MCMC algorithm generally performs better than the plug-in algorithm under the Kullback-Leibler information criterion, and is as good as the plug-in algorithm under the mean integrated squared error (MISE) criterion. Numerical studies for five-dimensional data show that our algorithm is superior to the normal reference rule. Our MCMC algorithm is the first data-driven bandwidth selector for multivariate kernel density estimation that is applicable to data of any dimension.
30142199
Computing Iceberg Quotient Cubes with Bounding
In complex data warehouse applications, high dimensional data cubes can become very big. The quotient cube is attractive in that it not only summarizes the original cube but also it keeps the roll-up and drill-down semantics between cube cells. In this paper we study the problem of semantic summarization of iceberg cubes, which comprises only cells that satisfy given aggregation constraints. We propose a novel technique for identifying groups of cells based on bounding aggregates and an efficient algorithm for computing iceberg quotient cubes for monotone functions. Our experiments show that iceberg quotient cubes can reduce data cube sizes and our iceberg quotient cubing algorithm can be over 10-fold more efficient than the current approach.
26512495
Multiscale tumor detection and segmentation in mammograms
Breast cancer is a leading cause of death among women. Automatic computer-aided analysis method can help radiologists to make more reliable and efficient decisions. Tumors and other abnormalities are of basic interests that need to be segmented and extracted in mammograms. In this paper, a new wavelet-packet multiscale image segmentation method is presented first. By combining this new method with a multiscale region-based segmentation method, we develop a new generic systematic scheme to segment suspicious tumor areas with exact boundaries in mammograms based on multiscale analysis in both grayscale and space. Examples are presented to illustrate the effectiveness of the technique. The new segmentation scheme can also be used in analysis of other abnormal tissues in mammograms.