Next Article in Journal
Drug Delivery Systems from Self-Assembly of Dendron-Polymer Conjugates
Next Article in Special Issue
NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features
Previous Article in Journal
Simultaneous Quantification of Three Curcuminoids and Three Volatile Components of Curcuma longa Using Pressurized Liquid Extraction and High-Performance Liquid Chromatography
Previous Article in Special Issue
Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages
Open AccessArticle

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

Figure 1

MDPI and ACS Style

Fang, M.; Lei, X.; Cheng, S.; Shi, Y.; Wu, F.-X. Feature Selection via Swarm Intelligence for Determining Protein Essentiality. Molecules 2018, 23, 1569. https://doi.org/10.3390/molecules23071569

AMA Style

Fang M, Lei X, Cheng S, Shi Y, Wu F-X. Feature Selection via Swarm Intelligence for Determining Protein Essentiality. Molecules. 2018; 23(7):1569. https://doi.org/10.3390/molecules23071569

Chicago/Turabian Style

Fang, Ming; Lei, Xiujuan; Cheng, Shi; Shi, Yuhui; Wu, Fang-Xiang. 2018. "Feature Selection via Swarm Intelligence for Determining Protein Essentiality" Molecules 23, no. 7: 1569. https://doi.org/10.3390/molecules23071569

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop