ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree
Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1165765/full |
| _version_ | 1797857876781301760 |
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| author | Yanjuan Li Di Ma Dong Chen Yu Chen |
| author_facet | Yanjuan Li Di Ma Dong Chen Yu Chen |
| author_sort | Yanjuan Li |
| collection | DOAJ |
| description | Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods. |
| first_indexed | 2024-04-09T21:03:56Z |
| format | Article |
| id | doaj.art-49be971368904481984abbf055e4600f |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-09T21:03:56Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-49be971368904481984abbf055e4600f2023-03-29T04:54:33ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-03-011410.3389/fgene.2023.11657651165765ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision treeYanjuan Li0Di Ma1Dong Chen2Yu Chen3College of Electrical and Information Engineering, Quzhou University, Quzhou, ChinaCollege of Computer, Hangzhou Dianzi University, Hangzhou, ChinaCollege of Electrical and Information Engineering, Quzhou University, Quzhou, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, ChinaCancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.https://www.frontiersin.org/articles/10.3389/fgene.2023.1165765/fullanticancer peptidesprotein identificationbiological sequence analysismachine learningartificial intelligence |
| spellingShingle | Yanjuan Li Di Ma Dong Chen Yu Chen ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree Frontiers in Genetics anticancer peptides protein identification biological sequence analysis machine learning artificial intelligence |
| title | ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree |
| title_full | ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree |
| title_fullStr | ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree |
| title_full_unstemmed | ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree |
| title_short | ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree |
| title_sort | acp gbdt an improved anticancer peptide identification method with gradient boosting decision tree |
| topic | anticancer peptides protein identification biological sequence analysis machine learning artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1165765/full |
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