Synthesizing Regularity Exposing Attributes in Large Protein Databases
Year: 1993
Type: article
Abstract: This thesis describes a system that synthesizes regularity exposing attributes from large protein databases. After processing primary and secondary structure data, this system discovers an amino acid representation that captures what are thought to be the three most important amino acid characteristics (size, charge, and hydrophobicity) for tertiary structure prediction. A neural network trained using this 16 bit representation achieves a performance accuracy on the secondary structure prediction problem that is comparable to the one achieved by a neural network trained using the standard 24 bit amino acid representation. In addition, the thesis describes bounds on secondary structure prediction accuracy, derived using an optimal learning algorithm and the probably approximately correct (PAC) model.
Author: Michael de la Maza
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G7407884558, G8876996369
OpenAlex ID: W2139747063