Query-driven module discovery in microarray data

Query-driven module discovery in microarray data

MOTIVATION Existing (bi)clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression p...

The False Discovery Rate in Simultaneous Fisher and Adjusted Permutation Hypothesis Testing on Microarray Data

Background and Objectives: In recent years, new technologies have led to produce a large amount of data and in the field of biology, microarray technology has also dramatically developed. Meanwhile, the Fisher test is used to compare the control group with two or more experimental groups and also to detect the differentially expressed genes. In this study, the false discovery rate was investiga...

Knowledge Discovery in Microarray Data

We present a method of prioritizing potential drug targets based on their gene expression “signature”. Primary human pre-cursor neuronal cells were treated with three classes (antidepressant, antipsychotic, opiod receptor agonist) of psychoactive drugs for 24 hours. Microarray technology was used to capture expression of ~11, 000 genes induced by these three categories of drugs. It was demonstr...

cDNA Microarray Data Normalization

Query-Driven Knowledge Discovery via OLAP manipulations

We study KDD (Knowledge Discovery in Databases) processes on OLAP (multidimensional and multilevel) data from a query point of view. Focusing on association rule mining, we consider typical queries to cope with the pre-processing of multidimensional data and the post-processing of the discovered patterns as well. We use a model and a rule-based language stemming from the OLAP representation and...