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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Issue 2 • April-June 2005

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Displaying Results 1 - 14 of 14
  • [Front cover]

    Publication Year: 2005, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2005, Page(s): c2
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  • Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1

    Publication Year: 2005, Page(s):81 - 82
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  • Attribute clustering for grouping, selection, and classification of gene expression data

    Publication Year: 2005, Page(s):83 - 101
    Cited by:  Papers (40)  |  Patents (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2520 KB) | HTML iconHTML

    This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection, and classification. The partitioning of a relational table into attribute subgroups allows a small number of attributes within or across the groups to be selected for analysis. By... View full abstract»

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  • Joint classification and pairing of human chromosomes

    Publication Year: 2005, Page(s):102 - 109
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (394 KB) | HTML iconHTML

    We reexamine the problems of computer-aided classification and pairing of human chromosomes, and propose to jointly optimize the solutions of these two related problems. The combined problem is formulated into one of optimal three-dimensional assignment with an objective function of maximum likelihood. This formulation poses two technical challenges: 1) estimation of the posterior probability that... View full abstract»

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  • Semisupervised learning for molecular profiling

    Publication Year: 2005, Page(s):110 - 118
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1067 KB) | HTML iconHTML

    Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a s... View full abstract»

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  • Essential latent knowledge for protein-protein interactions: analysis by an unsupervised learning approach

    Publication Year: 2005, Page(s):119 - 130
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1224 KB) | HTML iconHTML

    Protein-protein interactions play a number of central roles in many cellular functions, including DNA replication, transcription and translation, signal transduction, and metabolic pathways. A recent increase in the number of protein-protein interactions has made predicting unknown protein-protein interactions important for the understanding of living cells. However, the protein-protein interactio... View full abstract»

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  • Markov encoding for detecting signals in genomic sequences

    Publication Year: 2005, Page(s):131 - 142
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (783 KB) | HTML iconHTML

    We present a technique to encode the inputs to neural networks for the detection of signals in genomic sequences. The encoding is based on lower-order Markov models which incorporate known biological characteristics in genomic sequences. The neural networks then learn intrinsic higher-order dependencies of nucleotides at the signal sites. We demonstrate the efficacy of the Markov encoding method i... View full abstract»

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  • The latent process decomposition of cDNA microarray data sets

    Publication Year: 2005, Page(s):143 - 156
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1512 KB) | HTML iconHTML

    We present a new computational technique (a software implementation, data sets, and supplementary information are available at http://www.enm.bris.ac.uk/lpd/) which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called latent process decomposition (LPD), is introduced i... View full abstract»

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  • Fold recognition by predicted alignment accuracy

    Publication Year: 2005, Page(s):157 - 165
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (521 KB) | HTML iconHTML

    One of the key components in protein structure prediction by protein threading technique is to choose the best overall template for a given target sequence after all the optimal sequence-template alignments are generated. The chosen template should have the best alignment with the target sequence since the three-dimensional structure of the target sequence is built on the sequence-template alignme... View full abstract»

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  • Dimension reduction-based penalized logistic regression for cancer classification using microarray data

    Publication Year: 2005, Page(s):166 - 175
    Cited by:  Papers (27)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (950 KB) | HTML iconHTML

    The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for... View full abstract»

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  • [Advertisement]

    Publication Year: 2005, Page(s): 176
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  • IEEE/ACM TCBB: Information for authors

    Publication Year: 2005, Page(s): c3
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  • [Back cover]

    Publication Year: 2005, Page(s): c4
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Aims & Scope

This bimonthly publishes archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Ying Xu
University of Georgia
xyn@bmb.uga.edu

Associate Editor-in-Chief
Dong Xu
University of Missouri
xudong@missouri.edu