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» Clustering Genes Using Heterogeneous Data Sources
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COOPIS
2004
IEEE
14 years 2 months ago
Learning Classifiers from Semantically Heterogeneous Data
Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
Doina Caragea, Jyotishman Pathak, Vasant Honavar
JIB
2007
88views more  JIB 2007»
13 years 11 months ago
Achieving k-anonymity in DataMarts used for gene expressions exploitation
Gene expression profiling is a sophisticated method to discover differences in activation patterns of genes between different patient collectives. By reasonably defining patient...
Konrad Stark, Johann Eder, Kurt Zatloukal
BICOB
2010
Springer
13 years 11 months ago
Integrative Biomarker Discovery for Breast Cancer Metastasis from Gene Expression and Protein Interaction Data Using Error-toler
Biomarker discovery for complex diseases is a challenging problem. Most of the existing approaches identify individual genes as disease markers, thereby missing the interactions a...
Rohit Gupta, Smita Agrawal, Navneet Rao, Ze Tian, ...
KDD
2003
ACM
133views Data Mining» more  KDD 2003»
14 years 11 months ago
Interactive Analysis of Gene Interactions Using Graphical gaussian model
DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that sho...
Xintao Wu, Yong Ye, Kalpathi R. Subramanian
EDBTW
2004
Springer
14 years 4 months ago
Clustering Structured Web Sources: A Schema-Based, Model-Differentiation Approach
Abstract. The Web has been rapidly “deepened” with the prevalence of databases online. On this “deep Web,” numerous sources are structured, providing schema-rich data– Th...
Bin He, Tao Tao, Kevin Chen-Chuan Chang