Classifying RNA secondary structures using support vector machines

In contrast to DNA, RNA prevails as a single strand. As a consequence of small self-complementary regions, RNA commonly exhibits an intricate secondary structure, consisting of relatively short, double helical segments alternated with single stranded regions. The amount of sequence data available is rising rapidly day by day. One of the problems encountered on a specific molecule is finding the relevant data between the massive number of other sequences to be done by reading lists with a short description of all new entries in large databases already existing. One of the main objectives of this work is to take the extracted structures of aligned ribosomal RNA sequences and their secondary structures and cluster them. The proposal is to apply existing dimensionality reduction algorithms to these extracted structures and then cluster them in a reduced dimensional space using Support Vector Machines.

Name thesis:degree_name

Master of Science in Computer Science - (M.S.)

Discipline thesis:degree_discipline

Computer Science

Year

2006

Author dc:creator

• Sunkara, Prathy Usha

Contributors dc:contributor

• Jason T. L. Wang
• Chengjun Liu
• Qun Ma

• Ribosomal RNA sequences
• Dimensionality reduction algorithms
• Clustered RNA
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/414

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1413