Combining Machine Learning and Optimization Techniques to Determine 3-D Structures of Polypeptides
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2794-2794.
Abstract
One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.
@inproceedings{ijcai2011p469,
title = {Combining Machine Learning and Optimization Techniques to Determine 3-D Structures of Polypeptides},
author = {Dorn, Márcio and Buriol, Luciana S. and Lamb, Luís C.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {2794--2794},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-469},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-469},
}
TY - CPAPER TI - Combining Machine Learning and Optimization Techniques to Determine 3-D Structures of Polypeptides AU - Dorn, Márcio AU - Buriol, Luciana S. AU - Lamb, Luís C. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 2794 EP - 2794 DO - 10.5591/978-1-57735-516-8/IJCAI11-469 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-469 ER -Copied
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