Combining Gene Expression Profiles and Drug Activity Patterns Analysis: A Relational Clustering Approach

The combined analysis of tissue micro array and drug response datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the amount and the complexity of biological data needs appropriate data mining m...

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Bibliographic Details
Published in:Journal of mathematical modelling and algorithms Vol. 9; no. 3; pp. 275 - 289
Main Authors: Fersini, Elisabetta, Messina, E., Archetti, F., Manfredotti, C.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.09.2010
Springer Nature B.V
Springer Verlag
Subjects:
Algorithms
Cancer
Cluster analysis
Clustering
Computer Science
Data Mining and Knowledge Discovery
Expected values
Gene expression
Investigations
Management Science
Mathematical analysis
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Operations Research
Optimization
Principal components analysis
Studies
NCI60 dataset analysis
Relational clustering
Pharmacogenomics
ISSN:1570-1166, 2214-2487, 1572-9214, 2214-2495
Online Access:Get full text
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Summary:The combined analysis of tissue micro array and drug response datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the amount and the complexity of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. The ultimate goal of this paper is to define a model which, given the gene expression profile related to a specific tumor tissue, could help in selecting a set of most responsive drugs. This is accomplished through an integrated framework based on a constraint-based clustering algorithm, called Relational K-Means, which groups cell lines using drug response information and taking into account cell-to-cell relationships derived from their gene expression profiles.
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ISSN:1570-1166
2214-2487
1572-9214
2214-2495
DOI:10.1007/s10852-010-9140-2