Results 251 to 260 of about 31,256,270 (297)

Microarray gene expression data analysis

2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), 2005
Image analysis is a crucial step in processing microarray data generated by gene expression studies, which have been used extensively in understanding the molecular mechanisms of injury and recovery. A novel image analysis method utilizing an efficient snake-based multichannel image segmentation algorithm is proposed to analyze the microarray data. The
Yuhua Ding, Jacqueline Fairley, George J. Vachtsevanos, Andrew Gardner, Petia Simeonova   +4 more
openaire   +1 more source

MOACO Biclustering of gene expression data

International Journal of Functional Informatics and Personalised Medicine, 2010
Many bioinformatics data sets come from DNA microarray experiments. Biclustering of gene expression data can identify genes with similar behaviour with respect to different conditions. Ant Colony Optimisation (ACO) algorithms have been shown to be effective problem solving strategies for a wide range of problem domains.
Junwan Liu, Zhoujun Li 0001, Xiaohua Hu 0001, Yiming Chen 0002   +3 more
openaire   +2 more sources

An incremental clustering of gene expression data

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
This paper presents an incremental clustering algorithm based on DGC, a density-based algorithm we developed earlier [1]. We experimented with real-life datasets and both methods perform satisfactorily. The methods have been compared with some well-known clustering algorithms and they perform well in terms of z-score cluster validity measure.
Rosy Das, Dhruba Kumar Bhattacharyya, Jugal K. Kalita   +2 more
openaire   +2 more sources

Classification with Gene Expression Data

2005
A survey is given of tasks related to the construction and evaluation of classifiers applied to a renal cell cancer data set. Balanced sample splitting, non-specific filtering, linear discriminant analysis, nearest-neighbor prediction, and support vector machines are all concretely illustrated using the MLInterfaces package. Evaluations based on single
openaire   +2 more sources

Models for microarray gene expression data

Journal of Biopharmaceutical Statistics, 2002
This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of
Mei-Ling Ting, Lee, Weining, Lu, G A, Whitmore, David, Beier   +3 more
openaire   +2 more sources

Hierarchical clustering of gene expression data

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings., 2003
Rapid development of biological technologies generates a huge amount of data, which provides a processing and global view of the gene expression levels across different conditions and over multiple stages. Analyzation and interpretation of these massive data is a challenging task.
Feng Luo 0001, Kun Tang, Latifur Khan
openaire   +2 more sources

Outlier analysis for gene expression data

Journal of Computer Science and Technology, 2004
The rapid developments of technologies that generate arrays of gene data enable a global view of the transcription levels of hundreds of thousands of genes simultaneously. The outlier detection problem for gene data has its importance but together with the difficulty of high dimensionality.
Chao Yan 0002, Guo-Liang Chen 0001, Yi-Fei Shen   +2 more
openaire   +1 more source

Analyzing Microarray Gene Expression Data

2004
A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists
McLachlan, G. J., Do, K., Ambroise, C
openaire   +5 more sources

Fuzzy Classification of Gene Expression Data

2007 IEEE International Fuzzy Systems Conference, 2007
Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine.
Gerald Schaefer, Tomoharu Nakashima, Yasuyuki Yokota, Hisao Ishibuchi   +3 more
openaire   +2 more sources

FCM for Gene Expression Bioinformatics Data

2009
Clustering analysis of data from DNA microarray hybridization studies is essential for evaluating and identifying biologically significant co-expressed genes. The K-means algorithm is one of the most widely used clustering technique. It attempts to solve the clustering problem by assigning each gene to a single cluster.
Kumar Dhiraj, Santanu Kumar Rath, Korra Sathya Babu   +2 more
openaire   +2 more sources