15273775
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
Microarray technology offers a high-throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small sample size in microarray data calls for effective computational methods. In this article, we propose a novel hybrid dimension reduction technique for classification that combines principal component analysis (PCA) and linear discriminant analysis (LDA)-hybrid PCA and LDA analysis. This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace. It offers more flexibility and a richer set of alternatives to LDA and PCA in the parametric space. In addition, we propose a boosted hybrid discriminant analysis (HDA), using the AdaBoost algorithm which provides a unified and stable solution to find close to the optimal PCA-LDA prediction result and also reduces computational complexity. Extensive experiments on the yeast cell cycle regulation data set show the superior performance of the hybrid analysis, as we explain.
40832727
Interactive Semisupervised Learning for Microarray Analysis
5390173
A Self-supervised Learning Framework for Classifying Microarray Gene Expression Data
It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.
35354920
Color text image binarization based on binary texture analysis
In this paper, a novel binarization algorithm for color text images is presented. This algorithm effectively integrates color clustering and binary texture analysis, and is capable of handling situations with complex backgrounds. In this algorithm, dimensionality reduction and graph theoretical clustering are first employed. As the result, binary images related to clusters can be obtained. Binary texture analysis is then performed on each candidate binary image. Two kinds of effective texture features, run-length histogram and spatial-size distribution related respectively, are extracted and explored. Cooperating with an LDA classifier, the optimal candidate of the best binarization effect is obtained. Experiments with images collected from Internet has been carried out and compared with existing techniques, both show the effectiveness of the algorithm.
36831968
Evaluating and Comparing Biomedical Term Identification Systems
In this paper, we propose a term identification system using conditional random fields (CRFs) on two biomedical datasets. Through employing several sets of experiments, we make a comprehensive investigation for different types of features. The final experimental results reflect that with carefully designed features i.e., adding not only the individual and dynamic features but also the combinational features, our system can identify biomedical terms with fairly high accuracy on both datasets, compared with other top systems already published in the literature.
42946887
An Approach to Integrating SIP in Converged Multimodal/Multimedia Communication Services
Abstract
In this paper, we present an approach of integrating SIP (Session Initiation Protocol) in converged multimodal/multimedia communication services. An extensible VoIPTeleserver for VoIP in SIP environment is described. It is based on the concept of dialogue system and Web convergence that separates the channel dependent media resources from the application dependent service creation and hosting environment. It supports XML based service applications for multiple channels including voice, DTMF, IM and chat over IP. The loosely coupled open architecture in our approach is highly extensible. We describe the concept and structure of VoIPTeleServer used in our approach in detail, which interfaces to the VoIP world through SIP signaling and works as a broker between the VoIP SIP environment and MTIP to deliver converged communication services. A prototype of VoIPTeleServer was implemented, and services and applications based on SIP and MTIP convergence are constructed. Special attention is given to the adverse effect of delay, jitter and packet loss for voice portal services over IP. In particular, case studies of DTMF service in voice portal under adverse channel conditions are performed. The compounding effects of multiple channel impairments to DTMF in voice portal services over IP are characterized. The potential high error rate of the DTMF service indicates that the data redundancy method as proposed in RFC 2198 is needed for DTMF in order to achieve reliable voice portal services over IP.

11689877
A Projection and Search Algorithm for the Bicluster Problem
The advent of microarray technologies has made the experimental study of gene expression faster and more efficient. It is an important task to analyze the gene expression data acquired from the microarray experiments. Biclustering is a popular method to search a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. In this paper, we propose a projection and search algorithm (PSA) for finding biclusters in the gene expression matrix. The algorithm adopts projection method to find a raw submatrix, then refines and extends the raw submatrix into a bicluster using greedy algorithms. We test the algorithm on simulation data and real data. The test results show that the algorithm has good performance on both simulation data and real data. Additionally, compared with other methods, PSA isnpsilat sensitive to noise level.
9822006
Named Entity Recognition in Biomedical Literature Using Two-Layer Support Vector Machines
A beam receiver secured to an upper portion of a blade of an earth working equipment can rotate in a horizontal plane and move in a vertical direction and, accordingly, can catch a laser beam from a beam projector regardless of a direction of the equipment. When the beam has been deviated out of the beam receiver, the direction of the deviation is detected so that the beam can be received by the beam receiver again. The height of the beam receiver relative to the blade can be adjusted and the level of the blade can thereby be controlled very accurately.