Biological Data Mining for Genomic Clustering Using Unsupervised Neural Learning

— The paper aims at designing a scheme for automatic identification of a species from its genome sequence. A set of 64 three-tuple keywords is first generated using the four types of bases: A, T, C and G. These keywords are searched on N randomly sampled genome sequences, each of a given length (10,000 elements) and the frequency count for each of the 4 = 64 keywords is performed to obtain a DNA-descriptor for each sample. Principal Component analysis is then employed on the DNA-descriptors for N sampled instances. The principal component analysis yields a unique feature descriptor for identifying the species from its genome sequence. The variance of the descriptors for a given genome sequence being negligible, the proposed scheme finds extensive applications in automatic species identification. An alternative approach to automatic species classification and identification of species using Self-Organizing Feature Map is also discussed in the paper. The computational map is trained by...

• Type: Journal
• Year: 2007
• Where: ENGL
• Authors: Shreyas Sen, Seetharam Narasimhan, Amit Konar
• Keywords: Automatic Species, ENGL 2007, Genome Sequence, Principal Component Analysis