A New Scheme for Essential Protein Identification Based on Uncertain Networks
Identifying essential proteins is important for not only understanding cellular activity but also detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction (PPI) network. Although, existing studies have f...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9001097/ |
| _version_ | 1818444194751971328 |
|---|---|
| author | Wei Liu Liangyu Ma Ling Chen Byeungwoo Jeon |
| author_facet | Wei Liu Liangyu Ma Ling Chen Byeungwoo Jeon |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Identifying essential proteins is important for not only understanding cellular activity but also detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction (PPI) network. Although, existing studies have focused on the topological features of the PPI network and the intrinsic characteristics of biological attributes. it is still a big challenge to further improve the prediction accuracy of essential proteins. Moreover, there are substantial amounts of false-positive data in PPI networks; thus, a PPI network should be modelled as an uncertain network. How to identify essential proteins more accurately and conveniently has become a research hotspot. In this paper, we proposed a new essential protein discovery method called ETB-UPPI on uncertain PPI networks. The algorithm detects essential proteins by integrating topological features with biological information. Experimental results on four Saccharomyces cerevisiae datasets have shown that ETB-UPPI can not only improve the prediction accuracy but also outperform other prediction methods, including the most commonly-used centrality measures (DC, SC, BC, IC, EC, and NC), topology-based methods (LAC) and biological-data-integrating methods (PeC, WDC, UDONC, LBCC, TEGS, and RSG). |
| first_indexed | 2024-12-14T19:12:04Z |
| format | Article |
| id | doaj.art-13709253801142d1a0bb4fab2c4a0596 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:12:04Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-13709253801142d1a0bb4fab2c4a05962022-12-21T22:50:41ZengIEEEIEEE Access2169-35362020-01-018339773398910.1109/ACCESS.2020.29748979001097A New Scheme for Essential Protein Identification Based on Uncertain NetworksWei Liu0https://orcid.org/0000-0001-8503-4063Liangyu Ma1https://orcid.org/0000-0002-6127-3980Ling Chen2https://orcid.org/0000-0002-2461-8660Byeungwoo Jeon3https://orcid.org/0000-0003-0148-3684College of Information Engineering, Yangzhou University, Yangzhou, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou, ChinaSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South KoreaIdentifying essential proteins is important for not only understanding cellular activity but also detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction (PPI) network. Although, existing studies have focused on the topological features of the PPI network and the intrinsic characteristics of biological attributes. it is still a big challenge to further improve the prediction accuracy of essential proteins. Moreover, there are substantial amounts of false-positive data in PPI networks; thus, a PPI network should be modelled as an uncertain network. How to identify essential proteins more accurately and conveniently has become a research hotspot. In this paper, we proposed a new essential protein discovery method called ETB-UPPI on uncertain PPI networks. The algorithm detects essential proteins by integrating topological features with biological information. Experimental results on four Saccharomyces cerevisiae datasets have shown that ETB-UPPI can not only improve the prediction accuracy but also outperform other prediction methods, including the most commonly-used centrality measures (DC, SC, BC, IC, EC, and NC), topology-based methods (LAC) and biological-data-integrating methods (PeC, WDC, UDONC, LBCC, TEGS, and RSG).https://ieeexplore.ieee.org/document/9001097/Essential proteinssimrank algorithmuncertain PPI networkbiological information |
| spellingShingle | Wei Liu Liangyu Ma Ling Chen Byeungwoo Jeon A New Scheme for Essential Protein Identification Based on Uncertain Networks IEEE Access Essential proteins simrank algorithm uncertain PPI network biological information |
| title | A New Scheme for Essential Protein Identification Based on Uncertain Networks |
| title_full | A New Scheme for Essential Protein Identification Based on Uncertain Networks |
| title_fullStr | A New Scheme for Essential Protein Identification Based on Uncertain Networks |
| title_full_unstemmed | A New Scheme for Essential Protein Identification Based on Uncertain Networks |
| title_short | A New Scheme for Essential Protein Identification Based on Uncertain Networks |
| title_sort | new scheme for essential protein identification based on uncertain networks |
| topic | Essential proteins simrank algorithm uncertain PPI network biological information |
| url | https://ieeexplore.ieee.org/document/9001097/ |
| work_keys_str_mv | AT weiliu anewschemeforessentialproteinidentificationbasedonuncertainnetworks AT liangyuma anewschemeforessentialproteinidentificationbasedonuncertainnetworks AT lingchen anewschemeforessentialproteinidentificationbasedonuncertainnetworks AT byeungwoojeon anewschemeforessentialproteinidentificationbasedonuncertainnetworks AT weiliu newschemeforessentialproteinidentificationbasedonuncertainnetworks AT liangyuma newschemeforessentialproteinidentificationbasedonuncertainnetworks AT lingchen newschemeforessentialproteinidentificationbasedonuncertainnetworks AT byeungwoojeon newschemeforessentialproteinidentificationbasedonuncertainnetworks |