Functional Gene Detection and Clustering from Seed Gene Sets

Abstract

The availability of rapidly increasing repositories of micro array data requires the help of computer-aided analysis techniques. This data combined with a growing knowledge base about molecular processes enables the use of intelligent machine learning algorithms to expand the existing knowledge base. In this paper, we propose a novel algorithm, namely iterated Hidden Markov Model, to query micro array expression data with genes known to be involved in the same function to produce novel genes involved with the same cellular function. We run this algorithm on publicly available benchmark data sets and show that it outperforms comparable machine learning approaches.