Feature Selection via Swarm Intelligence for Determining Protein Essentiality
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 pr...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-06-01
|
| Series: | Molecules |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1420-3049/23/7/1569 |
| _version_ | 1818131683312926720 |
|---|---|
| author | Ming Fang Xiujuan Lei Shi Cheng Yuhui Shi Fang-Xiang Wu |
| author_facet | Ming Fang Xiujuan Lei Shi Cheng Yuhui Shi Fang-Xiang Wu |
| author_sort | Ming Fang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T08:24:50Z |
| format | Article |
| id | doaj.art-fdeaa65d83b74e99a45c702dbd004a79 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-11T08:24:50Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-fdeaa65d83b74e99a45c702dbd004a792022-12-22T01:14:35ZengMDPI AGMolecules1420-30492018-06-01237156910.3390/molecules23071569molecules23071569Feature Selection via Swarm Intelligence for Determining Protein EssentialityMing Fang0Xiujuan Lei1Shi Cheng2Yuhui Shi3Fang-Xiang Wu4School of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, CanadaProtein 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.http://www.mdpi.com/1420-3049/23/7/1569feature selectionessential proteinflower pollination algorithmmachine learningprotein-protein interaction (PPI) network |
| spellingShingle | Ming Fang Xiujuan Lei Shi Cheng Yuhui Shi Fang-Xiang Wu Feature Selection via Swarm Intelligence for Determining Protein Essentiality Molecules feature selection essential protein flower pollination algorithm machine learning protein-protein interaction (PPI) network |
| title | Feature Selection via Swarm Intelligence for Determining Protein Essentiality |
| title_full | Feature Selection via Swarm Intelligence for Determining Protein Essentiality |
| title_fullStr | Feature Selection via Swarm Intelligence for Determining Protein Essentiality |
| title_full_unstemmed | Feature Selection via Swarm Intelligence for Determining Protein Essentiality |
| title_short | Feature Selection via Swarm Intelligence for Determining Protein Essentiality |
| title_sort | feature selection via swarm intelligence for determining protein essentiality |
| topic | feature selection essential protein flower pollination algorithm machine learning protein-protein interaction (PPI) network |
| url | http://www.mdpi.com/1420-3049/23/7/1569 |
| work_keys_str_mv | AT mingfang featureselectionviaswarmintelligencefordeterminingproteinessentiality AT xiujuanlei featureselectionviaswarmintelligencefordeterminingproteinessentiality AT shicheng featureselectionviaswarmintelligencefordeterminingproteinessentiality AT yuhuishi featureselectionviaswarmintelligencefordeterminingproteinessentiality AT fangxiangwu featureselectionviaswarmintelligencefordeterminingproteinessentiality |