Comparing Clustering Techniques for Real Microarray Data

2012 ◽  
Author(s):  
V. P. Gazi ◽  
E. Kayis
Keyword(s):  
Microarray Data ◽  
Clustering Techniques ◽  
Real Microarray Data

scholarly journals Integrated Use of Statistical-Based Approaches and Computational Intelligence Techniques for Tumors Classification Using Microarray

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Chia-Ding Hou ◽  
Yuehjen E. Shao
Keyword(s):  
Microarray Data ◽  
Computational Intelligence ◽  
Cross Validation ◽  
Expression Data ◽  
Data Set ◽  
Microarray Experiments ◽  
Tumors Classification ◽  
Microarray Chips ◽  
Real Microarray Data ◽  
Validation Experiments

With the recent development of biotechnologies, cDNA microarray chips are increasingly applied in cancer research. Microarray experiments can lead to a more thorough grasp of the molecular variations among tumors because they can allow the monitoring of expression levels in cells for thousands of genes simultaneously. Accordingly, how to successfully discriminate the classes of tumors using gene expression data is an urgent research issue and plays an important role in carcinogenesis. To refine the large dimension of the genes data and effectively classify tumor classes, this study proposes several hybrid discrimination procedures that combine the statistical-based techniques and computational intelligence approaches to discriminate the tumor classes. A real microarray data set was used to demonstrate the performance of the proposed approaches. In addition, the results of cross-validation experiments reveal that the proposed two-stage hybrid models are more efficient in discriminating the acute leukemia classes than the established single stage models.

Clustering Techniques

2011 ◽  
pp. 176-179
Author(s):  
Sheng Ma ◽  
Tao Li
Keyword(s):  
Machine Learning ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Data Organization ◽  
Clustering Techniques ◽  
Efficient Data ◽  
The Subject ◽  
Active Research ◽  
Document Organization ◽  
Clustering Data

Clustering data into sensible groupings as a fundamental and effective tool for efficient data organization, summarization, understanding, and learning has been the subject of active research in several fields, such as statistics (Hartigan, 1975; Jain & Dubes, 1988), machine learning (Dempster, Laird & Rubin, 1977), information theory (Linde, Buzo & Gray, 1980), databases (Guha, Rastogi & Shim, 1998; Zhang, Ramakrishnan & Livny, 1996), and bioinformatics (Cheng & Church, 2000) from various perspectives and with various approaches and focuses. From an application perspective, clustering techniques have been employed in a wide variety of applications, such as customer segregation, hierarchal document organization, image segmentation, microarray data analysis, and psychology experiments.

Expression and network analysis of Illumina microarray data

2009 ◽  
Author(s):  
Giovanni Coppola ◽  
Kellen Winden ◽  
Genevieve Konopka ◽  
Fuying Gao ◽  
Daniel Geschwind
Keyword(s):  
Network Analysis ◽  
Microarray Data

Clustering techniques for thyroid nodules malignancy inference in the era of personalized medicine

2020 ◽  
Author(s):  
Andrea Giani ◽  
de Souza Patricia Borges ◽  
Stefania Bartoletti ◽  
Flavio Morselli ◽  
Andrea Conti ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Thyroid Nodules ◽  
Clustering Techniques

A State of Art Approaches on Energy Efficient Clustering Techniques in WSN

2019 ◽  
Vol 7 (3) ◽  
pp. 50-54
Author(s):  
N. Thilagavathi ◽  
Christy Wood ◽  
V. Hemalakshumi ◽  
V. Mathumiithaa
Keyword(s):  
Energy Efficient ◽  
Clustering Techniques ◽  
Energy Efficient Clustering ◽  
State Of Art
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