The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction
Date of Award
Master of Science (MS)
Yanqing Zhang - Chair
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.
Clayton, Arnshea, "The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction." Thesis, Georgia State University, 2006.