THEA: ontology-driven analysis of microarray data

THEA: ontology-driven analysis of microarray data

MOTIVATION Microarray technology makes it possible to measure thousands of variables and to compare their values under hundreds of conditions. Once microarray data are quantified, normalized and classified, the analysis phase is essentially a manual and subjective task based on visual inspection of classes in the light of the vast amount of information available. Currently, data interpretation ...

cDNA Microarray Data Normalization

Data Driven Ontology Evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain...

Ontology-driven Formal Conceptual Data Modeling for Biological Data Analysis

Biological data modeling serves many purposes, and many approaches exist that are used in this endeavor. The main topics of advanced conceptual data modeling for database and Object-Oriented software development to support biological data analysis are included in Figure 1.1, which extend the traditional ‘waterfall’ software development methodology as depicted in bold in Figure 1.2. The scope of...

Outcome-Driven Cluster Analysis with Application to Microarray Data

One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additio...