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A Markov Random Field model of microarray gridding

Mathias Katzer, Franz Kummert, Gerhard Sagerer
2003 Proceedings of the 2003 ACM symposium on Applied computing - SAC '03  
In this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by Markov Random Field (MRF) techniques.  ...  Our MRF model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm.  ...  We would also like to mention the researchers who made this work possible by providing microarray images: Anke Becker (Universität Bielefeld, Bielefeld, Germany), Volker Brendel (Iowa State University,  ... 
doi:10.1145/952548.952551 fatcat:zwjpyqmmzrgg7mtijrr3zfd5tm

A Markov Random Field model of microarray gridding

Mathias Katzer, Franz Kummert, Gerhard Sagerer
2003 Proceedings of the 2003 ACM symposium on Applied computing - SAC '03  
In this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by Markov Random Field (MRF) techniques.  ...  Our MRF model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm.  ...  We would also like to mention the researchers who made this work possible by providing microarray images: Anke Becker (Universität Bielefeld, Bielefeld, Germany), Volker Brendel (Iowa State University,  ... 
doi:10.1145/952532.952551 dblp:conf/sac/KatzerKS03 fatcat:z3ir7enaazeotepzdabez7fo4u

Microarray image gridding with stochastic search based approaches

Giuliano Antoniol, Michele Ceccarelli
2007 Image and Vision Computing  
This is done by modeling the solution a Markov random field with a Gibbs prior possibly containing first order cliques (1-clique).  ...  The paper reports a novel approach for the problem of automatic gridding in Microarray images.  ...  ;ng be the reference grid computed as reported in Section 2.1, then the joint distribution of G is modeled by a Gaussian Markov random field: PðGÞf exp K 1 2 X i ðg i Kt i Þ T X K1 i ðg i Kt i Þ " # (1  ... 
doi:10.1016/j.imavis.2006.01.023 fatcat:cr7jb4k7vrdddnxt5lfj5iw4gm

Estimating Gene Signals From Noisy Microarray Images

Pinaki Sarder, Arye Nehorai, Paul H. Davis, Samuel L. Stanley
2008 IEEE Transactions on Nanobioscience  
In oligonucleotide microarray experiments, noise is a challenging problem, as biologists now are studying their organisms not in isolation but in the context of a natural environment.  ...  We assume a priori spot-shape information using a circular outer periphery with an elliptical center hole. We assume Gaussian statistics for modeling both the foreground and background signals.  ...  The existing literature abounds in methods for automatic segmentation of the microarray images. In [4] , the authors propose Markov random field (MRF) and active-contour-based methods.  ... 
doi:10.1109/tnb.2008.2000745 pmid:18556262 pmcid:PMC4762609 fatcat:xutgw6rudff5hegl2srpsdmlzu

Annotating gene function by combining expression data with a modular gene network

Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
2007 Computer applications in the biosciences : CABIOS  
Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network.  ...  We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters.  ...  Hidden Markov random field (HMaF) A typical hidden random field is a hidden Markov random field, which we call HMaF in this article. 1 We explain this model briefly to show the significance of the dependency  ... 
doi:10.1093/bioinformatics/btm173 pmid:17646332 fatcat:appqt2o5srdylpa2yny7igplnm

Recursive computing and simulation-free inference for general factorizable models

N. Friel, H. Rue
2007 Biometrika  
All of the methods we describe apply to discrete-valued Markov random fields with nearest neighbour integrations defined on regular lattices; in particular we illustrate that exact inference can be performed  ...  We illustrate how the recursive algorithm of Reeves & Pettitt (2004) for general factorizable models can be extended to allow exact sampling, maximization of distributions and computation of marginal distributions  ...  ACKNOWLEDGEMENT Nial Friel wishes to thank the Department of Mathematical Sciences, Norwegian University of Science and Technology for their hospitality during April 2005.  ... 
doi:10.1093/biomet/asm052 fatcat:4jpo37qdcvg7nkhavaqit5tsym

A Fully Automated Method for Noisy cDNA Microarray Image Quantification

Islam A. Fouad, Mai S. Mabrouk, Amr A. Sharawy
2012 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
In this paper, a simple automated gridding technique is developed with a great effect on noisy microarray images.  ...  Most techniques divide the overall microarray image processing into three steps: gridding, segmentation, and quantification.  ...  The combination of Markov random field based grid segmentation and active contour modeling constitutes an approach suitable for spot detection and segmentation [7] .  ... 
doi:10.24297/ijct.v11i3.1170 fatcat:mcvp6cw6fbcknb7y4bzlld6ugi

A New Approach to Automatically Detecting Grids in DNA Microarray Images [chapter]

Luis Rueda, Vidya Vidyadharan
2005 Lecture Notes in Computer Science  
Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits highthroughput analysis  ...  Image and statistical analysis are two important aspects of microarray technology.  ...  Acknowledgements: This research work has been partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada, CFI, the Canadian Foundation for Innovation, and OIT, the Ontario  ... 
doi:10.1007/11559573_119 fatcat:xthjm5atanatppje6vlysfpnci

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

Emmanouil Athanasiadis, Dionisis Cavouras, Spyros Kostopoulos, Dimitris Glotsos, Ioannis Kalatzis, George Nikiforidis
2011 Computer Methods and Programs in Biomedicine  
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 4 ( 2 0 1 1 ) 307-315 Image segmentation Wavelet a b s t r a c t In the present study, an adaptation of the Markov Random  ...  Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF).  ...  Discussion In this study, we propose a novel generalised wavelet-based MRF (WMRF) segmentation model in segmenting microarray images.  ... 
doi:10.1016/j.cmpb.2011.03.007 pmid:21531035 fatcat:yzb7nrhuenhq7byixck6gqrjsy

An Unsupervised and Fully-Automated Image Analysis Method for cDNA Microarrays

E. Zacharia, D. Maroulis
2007 Computer-Based Medical Systems (CBMS), Proceedings of the IEEE Symposium on  
Microarray gene expression image analysis is a labor-intensive task and requires human intervention since microarray images are contaminated with noise and artifacts while spots are often poorly contrasted  ...  The first genetic algorithm determines the optimal grid while the second one determines, in parallel, the boundaries of multiple spots.  ...  Amongst other well-known techniques, the axis projections method [9] , the morphological method [10] , the Markov random field [11] , and the template matching and seeded region growing method [12]  ... 
doi:10.1109/cbms.2007.22 dblp:conf/cbms/ZachariaM07 fatcat:s4dhutvffvhwbmztwtnzprkway

Applications of Multilevel Thresholding Algorithms to Transcriptomics Data [chapter]

Luis Rueda, Iman Rezaeian
2011 Lecture Notes in Computer Science  
Microarrays are one of the methods for analyzing the expression levels of genes in a massive and parallel way.  ...  We show that these algorithms can be used for transcriptomics and genomics data analysis such as sub-grid and spot detection in DNA microarrays, and also for detecting significant regions based on next  ...  This work has been partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada.  ... 
doi:10.1007/978-3-642-25085-9_3 fatcat:ue5rsnild5bdznctei3yvjuyd4

Spot defects detection in cDNA microarray images

Mónica G. Larese, Pablo M. Granitto, Juan C. Gómez
2011 Pattern Analysis and Applications  
Each spot in the microarray contains the hybridization level of a single gene.  ...  The obtained displacement vectors are used to generate a grid template which overlaps the original image.  ...  At the moment, proofs are being made using Markov Random Fields (MRF) [20] .  ... 
doi:10.1007/s10044-011-0234-x fatcat:utpihm67qnd2zpqig2k53zup34

Mixture model analysis of DNA microarray images

K. Blekas, N.P. Galatsanos, A. Likas, I.E. Lagaris
2005 IEEE Transactions on Medical Imaging  
Index Terms-Cross-validated likelihood, DNA microarray image analysis, expectation-maximization algorithm, Gaussian mixture models, Markov random fields, maximum a posteriori, maximum likelihood, microarray  ...  In this paper, we propose a new methodology for analysis of microarray images. First, a new gridding algorithm is proposed for determining the individual spots and their borders.  ...  The latter takes also into account prior knowledge about the spatial assignment of the pixel labels using a Markov random field (MRF) model [13] .  ... 
doi:10.1109/tmi.2005.848358 pmid:16011320 fatcat:qhadyv6y2bcobmy2xazoswtrye

XMRF: an R package to fit Markov Networks to high-throughput genetics data

Ying-Wooi Wan, Genevera I. Allen, Yulia Baker, Eunho Yang, Pradeep Ravikumar, Matthew Anderson, Zhandong Liu
2016 BMC Systems Biology  
Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing  ...  Background Markov random fields (MRFs) are a popular tool for estimating relationships between genes, finding regulatory pathways, and visually depicting genetic networks.  ...  This article has been published as part of BMC Systems Biology Volume  ... 
doi:10.1186/s12918-016-0313-0 pmid:27586041 pmcid:PMC5009817 fatcat:fjmghbciqzf4bh2sgs5v7so3ku

Gridding spot centers of smoothly distorted microarray images

Jinn Ho, Wen-Liang Hwang, Henry Horn-Shing Lu, D.T. Lee
2006 IEEE Transactions on Image Processing  
His research interests include the design and analysis of algorithms, computational geometry, VLSI layout, web-based computing, algorithm visualization, software security, bio-informatics, digital libraries  ...  Chang of the Chinese Culture University, who posed the problem to them; and Prof. W.-H. Li of the University of Chicago for his insightful suggestions.  ...  From a set of coarse to fine thresholds , the estimation of can be simplified by a Markov random field approach, which yields (10) The transform of the initial threshold is obtained by applying the geometrical  ... 
doi:10.1109/tip.2005.860610 pmid:16479804 fatcat:juuklqyz6ncind2x5rev4tvlby
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