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Molecules 2018, 23(7), 1569; https://doi.org/10.3390/molecules23071569

Feature Selection via Swarm Intelligence for Determining Protein Essentiality

1
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
2
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
3
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
*
Authors to whom correspondence should be addressed.
Received: 25 May 2018 / Revised: 22 June 2018 / Accepted: 25 June 2018 / Published: 28 June 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Protein essentiality is fundamental to comprehend the function and evolution of genes. The prediction of protein essentiality is pivotal in identifying disease genes and potential drug targets. Since the experimental methods need many investments in time and funds, it is of great value to predict protein essentiality with high accuracy using computational methods. In this study, we present a novel feature selection named Elite Search mechanism-based Flower Pollination Algorithm (ESFPA) to determine protein essentiality. Unlike other protein essentiality prediction methods, ESFPA uses an improved swarm intelligence–based algorithm for feature selection and selects optimal features for protein essentiality prediction. The first step is to collect numerous features with the highly predictive characteristics of essentiality. The second step is to develop a feature selection strategy based on a swarm intelligence algorithm to obtain the optimal feature subset. Furthermore, an elite search mechanism is adopted to further improve the quality of feature subset. Subsequently a hybrid classifier is applied to evaluate the essentiality for each protein. Finally, the experimental results show that our method is competitive to some well-known feature selection methods. The proposed method aims to provide a new perspective for protein essentiality determination. View Full-Text
Keywords: feature selection; essential protein; flower pollination algorithm; machine learning; protein-protein interaction (PPI) network feature selection; essential protein; flower pollination algorithm; machine learning; protein-protein interaction (PPI) network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Fang, M.; Lei, X.; Cheng, S.; Shi, Y.; Wu, F.-X. Feature Selection via Swarm Intelligence for Determining Protein Essentiality. Molecules 2018, 23, 1569.

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