Functional Gene Detection and Clustering from Seed Gene Sets
- Resource Type
- Conference
- Authors
- Senf, Alexander; Chen, Xue-wen
- Source
- 2011 IEEE International Conference on Bioinformatics and Biomedicine Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on. :179-184 Nov, 2011
- Subject
- Bioengineering
Computing and Processing
Hidden Markov models
Clustering algorithms
Machine learning algorithms
Algorithm design and analysis
Gene expression
Training data
Functional modules
HMM
- Language
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.