Type of Document Master's Thesis Author Clayton, Arnshea Author's Email Address aclayton1@student.gsu.edu URN etd-02072006-103620 Title The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Yanqing Zhang Committee Chair Rajeshekhar Sunderraman Committee Member Yi Pan Committee Member Keywords
- Neural Networks
- Learning Vector Quantization
- Protein Secondary Structure Prediction
- Resilient Propagation
Date of Defense 2006-01-09 Availability unrestricted Abstract In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy.Files
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higher-speed Access clayton_arnshea_200605_ms.pdf 402.38 Kb 00:01:51 00:00:57 00:00:50 00:00:25 00:00:02