Exploring biomass energy of microorganisms using data mining methods

Energy crisis is a global issue and biomass energy is treated as a potential alternative energy. Biomass energy is a renewable energy that is converted by the use of abundant biomass. Archaea, which are suitable microorganisms for biomass converting into biomass energy, can survive under ammonia oxidation environment and release energy through the genetic metabolism. In this study, we analyzed and classified 27 kinds of Archaea, by using Fuzzy C-Means algorithm. Based on the concept of genetic metabolism, “codon usage bias” of three amino acids, Leucine, Serine and Arginine in Archaea, were chosen as the source for cluster analysis. Results showed a strong relationship between the finding clusters and traditional biological classifications, especially for the “Codon Usage Number” of Leucine. It is concluded that No. 15, No. 21 and No. 23, which have significant correlation with biological classification due to the same Genus species, would be found out as the potential Archaea by Fuzzy C-Means algorithm for biomass conversion. In summary, this study provides a method of clustering analysis to explore the microorganism for biomass.