1085378
Profiling alternatively spliced mRNA isoforms for prostate cancer classification
BackgroundProstate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.ResultsAs many as 464 splice isoforms from more than 200 genes are differentially regulated in tumors at a false discovery rate (FDR) of 0.05. Remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mRNA level. A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%. It is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account.ConclusionThese results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays.
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
12113641
Neighbor number, valley seeking and clustering
This paper proposes a novel nonparametric clustering algorithm capable of identifying shape-free clusters. This algorithm is based on a nonparametric estimation of the normalized density derivative (NDD) and the local convexity of the density distribution function, both of which are represented in a very concise form in terms of neighbor numbers. We use NDD to measure the dissimilarity between each pair of observations in a local neighborhood and to build a connectivity graph. Combined with the local convexity, this similarity measure can detect observations in local minima (valleys) of the density function, which separate observations in different major clusters. We demonstrate that this algorithm has a close relationship with the single-linkage hierarchical clustering and can be viewed as its extension. The performance of the algorithm is tested with both synthetic and real datasets. An example of color image segmentation is also given. Comparisons with several representative existing algorithms show that the proposed method can robustly identify major clusters even when there are complex configurations and/or large overlaps.
11474786
A Self-Reconfigurable Camera Array
This paper presents a self-reconfigurable camera array system that captures video sequences from an array of mobile cameras, renders novel views on the fly and reconfigures the camera positions to achieve better rendering quality. The system is composed of 48 cameras mounted on mobile platforms. The contribution of this paper is twofold. First, we propose an efficient algorithm that is capable of rendering high-quality novel views from the captured images. The algorithm reconstructs a view-dependent multi-resolution 2D mesh model of the scene geometry on the fly and uses it for rendering. The algorithm combines region of interest (ROI) identification, JPEG image decompression, lens distortion correction, scene geometry reconstruction and novel view synthesis seamlessly on a single Intel Xeon 2.4 GHz processor, which is capable of generating novel views at 4-10 frames per second (fps). Second, we present a view-dependent adaptive capturing scheme that moves the cameras in order to show even better rendering results. Such camera reconfiguration naturally leads to a nonuniform arrangement of the cameras on the camera plane, which is both view-dependent and scene-dependent.
10875476
Affinity relation discovery in image database clustering and content-based retrieval
In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
33375582
Edge coloring of graphs with small average degrees
Let G be a simple graph with average degree d? and maximum degree Δ. It is proved, in this paper that G is not critical if d?≤6 and Δ?8, or d?≤203 and Δ?9. This result generalizes earlier results of Vizing (Metody Diskret. Analiz. 5 (1965) 9), Mel'nikov (Mat. Zametki 7 (1970) 671) and Hind and Zhao (Discrete Math. 190 (1998) 107) and Yan and Zhao (Graphs Combin. 16 (2) (2000) 245). It also improves a result by Fiorini (Math. Proc. Cambridge Philos. Soc. 77 (1975) 475) on the number of edges of critical graphs for certain Δ.
14274462
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Algorithm based clustering method to reduce the search space. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm is demonstrated in comparative studies.
2688646
A unified framework for image database clustering and content-based retrieval
With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called <i>Markov Model Mediators</i> (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.