Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes
In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate ge...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2018/8124950 |
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| _version_ | 1832554309401706496 |
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| author | Runyu Jing Yu Liang Yi Ran Shengzhong Feng Yanjie Wei Li He |
| author_facet | Runyu Jing Yu Liang Yi Ran Shengzhong Feng Yanjie Wei Li He |
| author_sort | Runyu Jing |
| collection | DOAJ |
| description | In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets. |
| format | Article |
| id | doaj-art-b9cab1804206422b855e7b13ed180ce1 |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-b9cab1804206422b855e7b13ed180ce12025-02-03T05:51:55ZengWileyInternational Journal of Genomics2314-436X2314-43782018-01-01201810.1155/2018/81249508124950Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related GenesRunyu Jing0Yu Liang1Yi Ran2Shengzhong Feng3Yanjie Wei4Li He5Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaCollege of Chemistry, Sichuan University, Chengdu 610064, ChinaBiogas Appliance Quality Supervision and Inspection Center, Biogas Institute of Ministry of Agriculture, Chengdu, Sichuan, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaBiogas Appliance Quality Supervision and Inspection Center, Biogas Institute of Ministry of Agriculture, Chengdu, Sichuan, ChinaIn genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.http://dx.doi.org/10.1155/2018/8124950 |
| spellingShingle | Runyu Jing Yu Liang Yi Ran Shengzhong Feng Yanjie Wei Li He Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes International Journal of Genomics |
| title | Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes |
| title_full | Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes |
| title_fullStr | Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes |
| title_full_unstemmed | Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes |
| title_short | Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes |
| title_sort | ensemble methods with voting protocols exhibit superior performance for predicting cancer clinical endpoints and providing more complete coverage of disease related genes |
| url | http://dx.doi.org/10.1155/2018/8124950 |
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