An effective fuzzy kernel clustering analysis approach for gene expression data

2015 ◽  
Vol 26 (s1) ◽  
pp. S1863-S1869 ◽  
Author(s):  
Lin Sun ◽  
Jiucheng Xu ◽  
Jiaojiao Yin
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Analysis Approach ◽  
Expression Data ◽  
Kernel Clustering

Clustering analysis for gene expression data

1999 ◽  
Author(s):  
Yidong Chen ◽  
Olga Ermolaeva ◽  
Michael L. Bittner ◽  
Paul S. Meltzer ◽  
Jeffrey M. Trent ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Expression Data

A multivariate analysis approach to the integration of proteomic and gene expression data

PROTEOMICS ◽  
2007 ◽  
Vol 7 (13) ◽  
pp. 2162-2171 ◽  
Author(s):  
Ailís Fagan ◽  
Aedín C. Culhane ◽  
Desmond G. Higgins
Keyword(s):  
Gene Expression ◽  
Multivariate Analysis ◽  
Gene Expression Data ◽  
Analysis Approach ◽  
Expression Data

scholarly journals Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

2003 ◽  
Vol 19 (9) ◽  
pp. 1079-1089 ◽  
Author(s):  
G. Getz ◽  
H. Gal ◽  
I. Kela ◽  
D. A. Notterman ◽  
E. Domany
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Colon Cancer ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Cancer Gene ◽  
Expression Data

scholarly journals Clustering analysis of tumor metabolic networks

2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Ichcha Manipur ◽  
Ilaria Granata ◽  
Lucia Maddalena ◽  
Mario R. Guarracino
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Biological Networks ◽  
Clustering Analysis ◽  
Large Scale ◽  
Metabolic Networks ◽  
Computational Time ◽  
Expression Data ◽  
Metabolic Models

Abstract Background Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

Clustering Analysis of Gene Expression Data based on Semi-supervised Visual Clustering Algorithm

2006 ◽  
Vol 10 (11) ◽  
pp. 981-993 ◽  
Author(s):  
Fu-lai Chung ◽  
Shitong Wang ◽  
Zhaohong Deng ◽  
Chen Shu ◽  
D. Hu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Expression Data ◽  
Visual Clustering
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