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Signal Processing, IEEE Transactions on

Issue 6  Part 2 • Date June 2006

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Displaying Results 1 - 16 of 16
  • Table of contents

    Page(s): c1
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  • IEEE Transactions on Signal Processing publication information

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  • Optimal infinite-horizon control for probabilistic Boolean networks

    Page(s): 2375 - 2387
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (856 KB) |  | HTML iconHTML  

    External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Heretofore, intervention has focused on finite-horizon control, i.e., control over a small number of stages. This paper considers the design of optimal infinite-horizon control for context-sensitive probabilistic Boolean networks (PBNs). It can also be applied to instantaneously random PBNs. The stationary policy obtained is independent of time and dependent on the current state. This paper concentrates on discounted problems with bounded cost per stage and on average-cost-per-stage problems. These formulations are used to generate stationary policies for a PBN constructed from melanoma gene-expression data. The results show that the stationary policies obtained by the two different formulations are capable of shifting the probability mass of the stationary distribution from undesirable states to desirable ones. View full abstract»

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  • Detection-theoretic analysis of MatInspector

    Page(s): 2388 - 2393
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (272 KB) |  | HTML iconHTML  

    The goal of this paper is twofold. First, we wish to raise awareness in the signal processing community of research problems related to transcription factor binding site (TFBS) finding. This class of problems is important in the study of gene expression and regulation and is similar to the problems studied in statistical signal processing and detection theory. Second, we analyze one of the very popular tools for TFBS finding-MatInspector-from a detection-theoretic viewpoint. They show that MatInspector's test statistic is suboptimal, but its performance is reasonably close to that of the optimal Neyman-Pearson detector. View full abstract»

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  • Nonlinear modeling of protein expressions in protein arrays

    Page(s): 2394 - 2407
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1616 KB) |  | HTML iconHTML  

    This paper addresses the problem of estimating the expressions or concentrations of proteins from measurements obtained from protein arrays and illustrates the methodology on lysate microarray data. With several families of parametric models we design a number of algorithms for the estimation of a highly nonlinear calibration curve as well as the concentrations themselves. The model families include polynomial and sigmoidal nonlinearities for the calibration curve and homoscedastic or heteroscedastic models for the noise. The accuracy of the estimation methods is tested on simulated data and applied to real lysate array data. The results are generally very good, provided that strongly nonlinear models are used. View full abstract»

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  • Parallel identification of gene biclusters with coherent evolutions

    Page(s): 2408 - 2417
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (640 KB)  

    Finding clusters of genes with expression levels that evolve coherently under subsets of conditions can help uncover genetic pathways. This can be done by applying a biclustering procedure to gene expression data. Given a microarray data set with M genes and N conditions, we define a bicluster with coherent evolution as a subset of genes with expression levels that are nondecreasing as a function of a particular ordered subset of conditions. We propose a new biclustering procedure that identifies all biclusters with a specified number of K conditions in parallel with O(MK) complexity. Unlike almost all prior biclustering techniques, the proposed approach is guaranteed to find all biclusters with a specified minimum numbers of genes and conditions in the data set. All of the biclusters it identifies have no imperfection, i.e., the evolutions of the genes in each bicluster will be coherent across all conditions in the bicluster. Furthermore, the complexity of the proposed approach is lower than that of prior approaches. View full abstract»

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  • Identifying differentially expressed genes in microarray experiments with model-based variance estimation

    Page(s): 2418 - 2426
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (848 KB)  

    Statistical tests have been employed to identify genes differentially expressed under different conditions using data from microarray experiments. The variance of gene expression levels is often required in various statistical tests; however, due to the small number of replicates, the variance estimated from the sample variance is not accurate, which causes large false positive and negative errors. More accurate and robust variance estimation is thus highly desirable to improve the performance of statistical tests. In this paper, cluster analysis was performed on the microarray data using a model-based clustering method. The variance for each gene was then estimated from cluster variances. Since cluster variances are estimated from multiple genes whose microarray data have similar variance, the proposed estimation method pools the relevant genes together; this effectively increases the number of samples in variance estimation, thereby improving variance estimation. Using simulated data, it is shown that with the novel variance estimation, the performance of the t-test, regularized t-test, and a variant of SAM test, which is called the S-test here, can be improved. Using colon microarray data of Alon et al., it is demonstrated that the proposed method offers better or comparable performance compared with other gene pooling methods. Using the IHF microarray data of Arfin et al., it is shown that the proposed novel variance estimation decreases the significance of those genes having a small fold change but a high significant score assigned by the t-test using the sample variance, which potentially reduces false positive probability. View full abstract»

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  • Joint learning of logic relationships for studying protein function using phylogenetic profiles and the rosetta stone method

    Page(s): 2427 - 2435
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1016 KB) |  | HTML iconHTML  

    Identifying logic relationships between proteins is essential for understanding their function within cells. Previous studies have been done to infer protein logic relationships using pairwise and triplet logic analysis on phylogenetic profiles. Other computational methods have also been developed using pairwise analysis on Rosetta Stone data to infer protein functional linkages. (Proteins that share the same metabolic pathway or a common structural complex are said to be functionally linked.) This paper describes a Bayesian modeling framework for combining phylogenetic profile data via a likelihood with Rosetta Stone data via a prior probability. Based on the proposed framework, a general method is developed for jointly learning high-order logic relationships among proteins whose presence or absence can be identified by logic functions. The method is applied to analyze protein triplets and quartets on phylogenetic profile and Rosetta Stone data sets with 140 clusters of orthologous genes (COGs). The biological meaning of the top 30 significant triplets are further verified using the KEGG and NCBI databases. Over 50% of the discovered relationships that are associated with high significant scores could not be inferred using phylogenetic profile or Rosetta Stone data alone. The statistical analysis in this paper shows that all significant quartets have p-values ≤5.71E-04. Many of them assign putative functional roles on uncharacterized proteins. View full abstract»

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  • Combining feature selection and DTW for time-varying functional genomics

    Page(s): 2436 - 2443
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (736 KB) |  | HTML iconHTML  

    Given temporal high-throughput data defining a two-class functional genomic process, feature selection algorithms may be applied to extract a panel of discriminating gene time series. We aim to identify the main trends of activity through time. A reconstruction method based on stagewise boosting is endowed with a similarity measure based on the dynamic time warping (DTW) algorithm, defining a ranked set of time-series component contributing most to the reconstruction. The approach is applied on synthetic and public microarray data. On the Cardiogenomics PGA Mouse Model of Myocardial Infarction, the approach allows the identification of a time-varying molecular profile of the ventricular remodeling process. View full abstract»

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  • A statistical model for microarrays, optimal estimation algorithms, and limits of performance

    Page(s): 2444 - 2455
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB) |  | HTML iconHTML  

    DNA microarray technology relies on the hybridization process, which is stochastic in nature. Currently, probabilistic cross hybridization of nonspecific targets, as well as the shot noise (Poisson noise) originating from specific targets binding, are among the main obstacles for achieving high accuracy in DNA microarray analysis. In this paper, statistical techniques are used to model the hybridization and cross-hybridization processes and, based on the model, optimal algorithms are employed to detect the targets and to estimate their quantities. To verify the theory, two sets of microarray experiments are conducted: one with oligonucleotide targets and the other with complementary DNA (cDNA) targets in the presence of biological background. Both experiments indicate that, by appropriately modeling the cross-hybridization interference, significant improvement in the accuracy over conventional methods such as direct readout can be obtained. This substantiates the fact that the accuracy of microarrays can become exclusively noise limited, rather than interference (i.e., cross-hybridization) limited. The techniques presented in this paper potentially increase considerably the signal-to-noise ratio (SNR), dynamic range, and resolution of DNA and protein microarrays as well as other affinity-based biosensors. A preliminary study of the Cramer-Rao bound for estimating the target concentrations suggests that, in some regimes, cross hybridization may even be beneficial-a result with potential ramifications for probe design, which is currently focused on minimizing cross hybridization. Finally, in its current form, the proposed method is best suited to low-density arrays arising in diagnostics, single nucleotide polymorphism (SNP) detection, toxicology, etc. How to scale it to high-density arrays (with many thousands of spots) is an interesting challenge. View full abstract»

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  • Phase-only filtering for the masses (of DNA Data): a new approach to sequence alignment

    Page(s): 2456 - 2466
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1000 KB) |  | HTML iconHTML  

    Alignment of DNA segments containing repetitive nucleotide base patterns is an important task in several genomics applications, including DNA sequencing, DNA fingerprinting, pathogen detection, and gene finding. One of the most efficient procedures used for this task is the cross correlation method. The main computations of the procedure are the discrete Fourier transform (DFT) and a pointwise multiplication of two complex Fourier transform sequences. In this paper, the standard magnitude-and-phase cross correlation technique is compared with the lesser known but closely related phase-only cross correlation method. It is shown that for a periodic DNA sequence, the standard approach leads to significant sidelobes in the cross correlation, the magnitude of which increases with sequence length, while the phase-only approach yields a perfect cross correlation with zero sidelobes. For a DNA sequence that contains both irregularly distributed symbols and periodic patterns, the difference in performance is less pronounced, but still significant. Numerical experiments on synthesized and real data demonstrate that the phase-only approach is robust to isolated symbol insertions and deletions and that it is capable of identifying positions of matching segments in the sequence. View full abstract»

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  • 2006 Workshop on Spoken Language Technology

    Page(s): 2467
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  • Special issue on content storage and delivery in peer-to-peer network

    Page(s): 2468
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  • IEEE Signal Processing Society Information

    Page(s): c3
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  • Blank page [back cover]

    Page(s): c4
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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Meet Our Editors

Editor-in-Chief
Zhi-Quan (Tom) Luo
University of Minnesota