University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Protein Attributes Microtuning System (PAMS): an effective tool to increase protein structure prediction by data purification

Zhang, Fan, Povey, David and Krause, Paul J. (2007) Protein Attributes Microtuning System (PAMS): an effective tool to increase protein structure prediction by data purification In: 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference.


Download (7MB)


Given the expense of more direct determinations, using machine-learning schemes to predict a protein secondary structure from the sequence alone remains an important methodology. To achieve significant improvements in prediction accuracy, the authors have developed an automated tool to prepare very large biological datasets, to be used by the learning network. By focusing on improvements in data quality and validation, our experiments yielded a highest prediction accuracy of protein secondary structure of 90.97%. An important additional aspect of this achievement is that the predictions are based on a template-free statistical modeling mechanism. The performance of each different classifier is also evaluated and discussed. In this paper a protein set of 232 protein chains are proposed to be used in the prediction. Our goal is to make the tools discussed available as services in part of a digital ecosystem that supports knowledge sharing amongst the protein structure prediction community.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Zhang, Fan
Krause, Paul
Date : 21 February 2007
DOI : 10.1109/DEST.2007.372039
Uncontrolled Keywords : Automata, Biomedical Computing, Data Management, Prediction Methods, Proteins
Additional Information : In Proceedings of the 2007 Inaugural IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2007(, pp. 565 - 570.© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User : Mr Adam Field
Date Deposited : 27 May 2010 14:46
Last Modified : 16 Jan 2019 16:23

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800