Results 1 to 10 of about 95,976 (110)

Association Analysis Techniques for Discovering Functional Modules from Microarray Data [PDF]

Nature Precedings, 2008
An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also ...
Gaurav Pandey, Gowtham Atluri, Michael Steinbach, Vipin Kumar   +3 more
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Microarray Technique, Analysis, and Applications in Dermatology [PDF]

Journal of Investigative Dermatology, 2013
The Human Genome Project, completed in 2003, identified the sequence of the approximately 25,000 genes that comprise the human genome. Knowledge of the structure of the human genome opened the doors to studying the actual function of specific genes, which is necessary to understanding health and disease.
Alex G. Ortega-Loayza, Jennifer Villaseñor-Park   +1 more
openaire   +3 more sources

Adaptive techniques for microarray image analysis with related quality assessment [PDF]

Journal of Electronic Imaging, 2007
We propose novel techniques for microarray image analysis. In particular, we describe an overall pipeline able to solve the most common problems of microarray image analysis. We pro- pose the microarray image rotation algorithm (MIRA) and the statis- tical gridding pipeline (SGRIP) as two advanced modules devoted to restoring the original microarray ...
BATTIATO, SEBASTIANO, G. DI BLASI, FARINELLA, GIOVANNI MARIA, GALLO, Giovanni, G. C. GUARNERA   +4 more
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MAPPI-DAT : data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform [PDF]

, 2017
Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for ...
De Puysseleyr, Veronic, Degroeve, Sven, Gupta, Surya, Lemmens, Irma, Lievens, Sam, Maddelein, Davy, Martens, Lennart, Tavernier, Jan, Van Der Heyden, José   +8 more
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Copasetic analysis: a framework for the blind analysis of microarray imagery [PDF]

, 2004
The official published version can be found at the link below.From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common ...
Berkhin, Bozinov, Bozinov, Cheriet, Dunn, Hartelius, Katzer, McQueen, Moore, Nagarajan, O'Neill, Otsu, Wang, Yang   +13 more
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Information visualization for DNA microarray data analysis: A critical review [PDF]

, 2008
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously.
Kuljis, J, Liu, X, Zhang, L
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Computational Intelligence Techniques for Classification in Microarray Analysis [PDF]

, 2010
During the last few years there has been a growing need for using computational intelligence techniques to analyze microarray data. The aim of the system presented in this study is to provide innovative decision support techniques for classifying data from microarrays and for extracting knowledge about the classification process.
Juan F. De Paz, Sara Rodríguez, Javier Bajo, Juan M. Corchado   +3 more
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Techniques for clustering gene expression data [PDF]

, 2007
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate
Crane, Martin, Doolan, Padraig, Kerr, Gráinne, Ruskin, Heather J.   +3 more
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Simcluster: clustering enumeration gene expression data on the simplex space [PDF]

, 2007
Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement.
Carlos A de B Pereira, E Dougherty, G Stolovitzky, H Thygesen, Helena Brentani, I Braslavsky, Ilya Shmulevich, J Aitchison, J Aitchison, K Okubo, L Cai, L Hood, Leonardo Varuzza, M Bainbridge, M Brun, M de Hoon, M Gilchrist, M Margulies, M Schena, N Bolshakova, R Loganantharaj, R Page, R Vencio, R Vencio, RF Service, Ricardo ZN Vêncio, S Audic, S Brenner, S Datta, S Fodor, T Seo, V Velculescu   +31 more
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Towards knowledge-based gene expression data mining [PDF]

, 2007
The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the
Allison, Andersson, Ashburner, Bar-Joseph, Bar-Joseph, Batagelj, Bernard, Blaž Zupan, Bolshakova, Brown, Cheng, Cheng, Cho, Clayton, Cooper, Curk, Datta, de Jong, D’Haeseleer, Eisen, Falcon, Friedman, Gamberger, Gavin, Geiger, Guo, Hand, Hand, Hastie, Huang, Hughes, Hughes, Hvidsten, Imoto, Jeffery, Jiang, Khan, Khatri, King, Kong, Lai, Le Phillip, Li, Liang, Lin, Mewes, Mitchell, Moskovitch, Nariai, Pan, Prelic, Qin, Raftery, Resnik, Riccardo Bellazzi, Riva, Rousu, Ruepp, Sacchi, Schlitt, Sebastiani, Shahar, Simon, Statnikov, Stuart, Subramanian, Tamada, Tamayo, Troyanskaya, van de Peppel, Van Driessche, Wang, Witten, Xing, Zupan   +74 more
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