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Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes
Joint Authors
Feng, Shengzhong
Liang, Yu
Wei, Yanjie
Jing, Runyu
Ran, Yi
He, Li
Source
International Journal of Genomics
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jing, Runyu& Liang, Yu& Ran, Yi& Feng, Shengzhong& Wei, Yanjie& He, Li. 2018. 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،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1172869
Modern Language Association (MLA)
Jing, Runyu…[et al.]. 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 No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1172869
American Medical Association (AMA)
Jing, Runyu& Liang, Yu& Ran, Yi& Feng, Shengzhong& Wei, Yanjie& He, Li. 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. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1172869
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172869