Microarrays: how many do you need?
Proceedings of the sixth annual international conference on Computational biology
Learning rough set classifiers from gene expressions and clinical data
Fundamenta Informaticae
Microarray data analysis with PCA in a DBMS
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Efficient computation of PCA with SVD in SQL
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
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Animal models for human diseases are of crucial importance for studying gene expression and regulation. In the last decade the development of mouse models for cancer, diabetes, neuro-degenerative and many other diseases has been on steady rise. Microarray analysis of patterns of gene expression in mouse models of various pathological types and the study of molecular level changes as a result of interventions, holds lot of promise to the understanding of biological processes involved. The genes which show normal variance across genetically identical mice are of particular interest because they could serve as a databank for possible false positives in gene expression studies in similar kind of mice. Also they could provide useful insights into the biological processes behind the differential expression patterns in otherwise similar mice. Our approach systematically removes variance due to experimental noise in each of the mice and then mines for normal variance among the identical mice. This analysis carried over six tissues sampled from mice, resulted in several genes which showed variations among identical mice, thus enabling a comprehensive database of normal variations in gene expression for mouse models. A large number of these genes are known to be related to stress response, hypertension and heat shock. Also Principal Component Analysis was done to visualize similarity among the mice models and within the replicates. These studies help in the design of gene expression studies in mouse models and help in validation of the results.