MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier
Predicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzyme...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9032150/ |
| _version_ | 1818479352847794176 |
|---|---|
| author | Xuan Xiao Li-Wen Duan Guang-Fu Xue Gang Chen Pu Wang Wang-Ren Qiu |
| author_facet | Xuan Xiao Li-Wen Duan Guang-Fu Xue Gang Chen Pu Wang Wang-Ren Qiu |
| author_sort | Xuan Xiao |
| collection | DOAJ |
| description | Predicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzymes. Actually, multi-functional enzymes should not be ignored because they usually possess diverse biological functions worthy of our special notice. By introducing the “improved Hybrid Multi-label Classifier” and “neighbor score”, a new predictor, called <bold>MF-EFP</bold>, has been developed that can be used to deal with the systems containing both mono-functional and multi-functional enzymes. As demonstration, the jackknife cross-validation was performed with MF-EFP on a benchmark dataset of enzymes classified into the following 7 functional classes: (1) EC 1 Oxidoreductase, (2) EC 2 Transferase, (3) EC 3 Hydrolase, (4) EC 4 Lyase, (5) EC 5 Isomerase, (6) EC 6 Ligase, (7) EC7 Translocases, where none of enzymes included has ≥90% pairwise sequence identity to any other in a same subset. The subset accuracy and average precision thus obtained by MF-EFP was 85.62% and 94.16% respectively. Extensive experiments also show that MF-EFP can outperform the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, MF-EFP is freely accessible to the public at the web-site <uri>http://www.jci-bioinfo.cn/MF-EFP</uri>. |
| first_indexed | 2024-12-10T11:09:28Z |
| format | Article |
| id | doaj.art-32a12ec1d4e943a698e601d3c486cd9b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:09:28Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-32a12ec1d4e943a698e601d3c486cd9b2022-12-22T01:51:27ZengIEEEIEEE Access2169-35362020-01-018502765028410.1109/ACCESS.2020.29798889032150MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label ClassifierXuan Xiao0Li-Wen Duan1Guang-Fu Xue2https://orcid.org/0000-0002-5718-2314Gang Chen3Pu Wang4Wang-Ren Qiu5https://orcid.org/0000-0001-7659-8553Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, ChinaComputer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, ChinaComputer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, ChinaComputer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, ChinaComputer School, Hubei University of Arts and Science, Xiangyang, ChinaComputer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, ChinaPredicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzymes. Actually, multi-functional enzymes should not be ignored because they usually possess diverse biological functions worthy of our special notice. By introducing the “improved Hybrid Multi-label Classifier” and “neighbor score”, a new predictor, called <bold>MF-EFP</bold>, has been developed that can be used to deal with the systems containing both mono-functional and multi-functional enzymes. As demonstration, the jackknife cross-validation was performed with MF-EFP on a benchmark dataset of enzymes classified into the following 7 functional classes: (1) EC 1 Oxidoreductase, (2) EC 2 Transferase, (3) EC 3 Hydrolase, (4) EC 4 Lyase, (5) EC 5 Isomerase, (6) EC 6 Ligase, (7) EC7 Translocases, where none of enzymes included has ≥90% pairwise sequence identity to any other in a same subset. The subset accuracy and average precision thus obtained by MF-EFP was 85.62% and 94.16% respectively. Extensive experiments also show that MF-EFP can outperform the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, MF-EFP is freely accessible to the public at the web-site <uri>http://www.jci-bioinfo.cn/MF-EFP</uri>.https://ieeexplore.ieee.org/document/9032150/Multi-functional enzymemulti-label learningneighbor scorehybrid methodfunction prediction |
| spellingShingle | Xuan Xiao Li-Wen Duan Guang-Fu Xue Gang Chen Pu Wang Wang-Ren Qiu MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier IEEE Access Multi-functional enzyme multi-label learning neighbor score hybrid method function prediction |
| title | MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier |
| title_full | MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier |
| title_fullStr | MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier |
| title_full_unstemmed | MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier |
| title_short | MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier |
| title_sort | mf efp predicting multi functional enzymes function using improved hybrid multi label classifier |
| topic | Multi-functional enzyme multi-label learning neighbor score hybrid method function prediction |
| url | https://ieeexplore.ieee.org/document/9032150/ |
| work_keys_str_mv | AT xuanxiao mfefppredictingmultifunctionalenzymesfunctionusingimprovedhybridmultilabelclassifier AT liwenduan mfefppredictingmultifunctionalenzymesfunctionusingimprovedhybridmultilabelclassifier AT guangfuxue mfefppredictingmultifunctionalenzymesfunctionusingimprovedhybridmultilabelclassifier AT gangchen mfefppredictingmultifunctionalenzymesfunctionusingimprovedhybridmultilabelclassifier AT puwang mfefppredictingmultifunctionalenzymesfunctionusingimprovedhybridmultilabelclassifier AT wangrenqiu mfefppredictingmultifunctionalenzymesfunctionusingimprovedhybridmultilabelclassifier |