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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Issue 3 • Date July-Sept. 2005

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Displaying Results 1 - 15 of 15
  • [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 Editor's Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 2

    Publication Year: 2005, Page(s):177 - 178
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  • Analyzing gene expression time-courses

    Publication Year: 2005, Page(s):179 - 193
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1302 KB) | HTML iconHTML

    Measuring gene expression over time can provide important insights into basic cellular processes. Identifying groups of genes with similar expression time-courses is a crucial first step in the analysis. As biologically relevant groups frequently overlap, due to genes having several distinct roles in those cellular processes, this is a difficult problem for classical clustering methods. We use a m... View full abstract»

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  • Combining sequence and time series expression data to learn transcriptional modules

    Publication Year: 2005, Page(s):194 - 202
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (568 KB) | HTML iconHTML

    Our goal is to cluster genes into transcriptional modules - sets of genes where similarity in expression is explained by common regulatory mechanisms at the transcriptional level. We want to learn modules from both time series gene expression data and genome-wide motif data that are now readily available for organisms such as S. cereviseae as a result of prior computational studies or experimental... View full abstract»

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  • Associative clustering for exploring dependencies between functional genomics data sets

    Publication Year: 2005, Page(s):203 - 216
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (875 KB) | HTML iconHTML

    High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set a... View full abstract»

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  • Predicting molecular formulas of fragment ions with isotope patterns in tandem mass spectra

    Publication Year: 2005, Page(s):217 - 230
    Cited by:  Papers (10)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1385 KB) | HTML iconHTML

    A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on isotope patterns, enumerate all possible formulas for an ion, and exclude certain formulas violating chemical constraints. However, these methods cannot be well generalized to the component prediction o... View full abstract»

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  • Discovering gene networks with a neural-genetic hybrid

    Publication Year: 2005, Page(s):231 - 242
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1550 KB) | HTML iconHTML

    Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and... View full abstract»

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  • The applicability of recurrent neural networks for biological sequence analysis

    Publication Year: 2005, Page(s):243 - 253
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1486 KB) | HTML iconHTML

    Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a ... View full abstract»

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  • Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation

    Publication Year: 2005, Page(s):254 - 261
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (487 KB) | HTML iconHTML

    We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its l... View full abstract»

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  • Learning the topological properties of brain tumors

    Publication Year: 2005, Page(s):262 - 270
    Cited by:  Papers (24)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (624 KB) | HTML iconHTML

    This work presents a graph-based representation (a.k.a., cell-graph) of histopathological images for automated cancer diagnosis by probabilistically assigning a link between a pair of cells (or cell clusters). Since the node set of a cell-graph can include a cluster of cells as well as individual ones, it enables working with low-cost, low-magnification photomicrographs. The contributions of this ... View full abstract»

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  • Call for Papers for Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics

    Publication Year: 2005, Page(s): 271
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  • [Advertisement]

    Publication Year: 2005, Page(s): 272
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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