Synthesizing Regularity Exposing Attributes in Large Protein Databases

Title: en_US Synthesizing Regularity Exposing Attributes in Large Protein Databases
Creator: en_US de la Maza, Michael
Date: 2004-10-20T19:55:04Z
Date Available: 2004-10-20T19:55:04Z
Date Issued: en_US 1993-05-01
Identifier: en_US AITR-1444
uri: http://hdl.handle.net/1721.1/6789
Abstract: en_US 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.
Extent: en_US 90 p.; 204397 bytes; 794429 bytes
Format: application/octet-stream; application/pdf
Language: en_US
Relation: en_US AITR-1444
Subject: en_US representation reformulation; en_US secondary structuresprediction; en_US genetic algorithms; en_US neural networks; en_US clustering algorithm; en_US sdecision tree systems