Journal
ANALYTICA CHIMICA ACTA
Volume 740, Issue -, Pages 20-26Publisher
ELSEVIER
DOI: 10.1016/j.aca.2012.06.031
Keywords
Variable selection; Gene expression-based disease classification; Markov Chain Monte Carlo; Random frog
Categories
Funding
- National Nature Foundation Committee of P.R. China [20875104, 21075138, 21105129]
- Graduate degree thesis Innovation Foundation of Central South University [CX2010B057]
- Fundamental Research Funds for the Central Universities [2011QNZT053]
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The identification of disease-relevant genes represents a challenge in microarray-based disease diagnosis where the sample size is often limited. Among established methods, reversible jump Markov Chain Monte Carlo (RJMCMC) methods have proven to be quite promising for variable selection. However, the design and application of an RJMCMC algorithm requires, for example, special criteria for prior distributions. Also, the simulation from joint posterior distributions of models is computationally extensive, and may even be mathematically intractable. These disadvantages may limit the applications of RJMCMC algorithms. Therefore, the development of algorithms that possess the advantages of RJMCMC methods and are also efficient and easy to follow for selecting disease-associated genes is required. Here we report a RJMCMC-like method, called random frog that possesses the advantages of RJMCMC methods and is much easier to implement. Using the colon and the estrogen gene expression datasets, we show that random frog is effective in identifying discriminating genes. The top 2 ranked genes for colon and estrogen are Z50753, U00968, and Y10871_at, Z22536_at, respectively. (The source codes with GNU General Public License Version 2.0 are freely available to non-commercial users at: http://code.google.com/p/randomfrog/.) (C) 2012 Published by Elsevier B.V.
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