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Moving Object Detection and Tracking Based on Three-Frame Difference and Background Subtraction with Laplace Filter Read chapter Read first chapter

2018 | OriginalPaper | Chapter

On the Use of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure Prediction

Authors : Óscar Álvarez, Juan Luis Fernández-Martínez, Celia Fernández-Brillet, Ana Cernea, Zulima Fernández-Muñiz, Andrzej Kloczkowski

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

We discuss applicability of Principal Component Analysis and Particle Swarm Optimization in protein tertiary structure prediction. The proposed algorithm is based on establishing a low-dimensional space where the sampling (and optimization) is carried out via Particle Swarm Optimizer (PSO). The reduced space is found via Principal Component Analysis (PCA) performed for a set of previously found low-energy protein models. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. Our results show that PSO improves the energy of the best decoy used in the PCA considering an adequate number of PCA terms.

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Metadata
Title
On the Use of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure Prediction
Authors
Óscar Álvarez
Juan Luis Fernández-Martínez
Celia Fernández-Brillet
Ana Cernea
Zulima Fernández-Muñiz
Andrzej Kloczkowski
Copyright Year
2018
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91261-5

Electronic ISBN: 978-3-319-91262-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91262-2_10

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