Developing cancer prediction model based on stepwise selection by AUC measure for proteomics data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim Y. | - |
dc.contributor.author | Lee S. | - |
dc.contributor.author | Kwon M.-S. | - |
dc.contributor.author | Na A. | - |
dc.contributor.author | Choi Y. | - |
dc.contributor.author | Yi S.G. | - |
dc.contributor.author | Namkung J. | - |
dc.contributor.author | Han S. | - |
dc.contributor.author | Kang M. | - |
dc.contributor.author | Kim S.W. | - |
dc.contributor.author | Jang J.-Y. | - |
dc.contributor.author | Kim Y. | - |
dc.contributor.author | Kim Y. | - |
dc.contributor.author | Park T. | - |
dc.date.available | 2021-02-22T11:32:08Z | - |
dc.date.issued | 2015-12 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/10162 | - |
dc.description.abstract | Since most of the cancer markers that have been reported are obtained directly from cancer tissues, it is difficult to use them for early diagnosis of cancer without surgery. Thus, development of markers that can be detected by blood is crucial for making early diagnosis of cancer easier. One of the most feasible types of markers that can be detected by blood is a protein marker. Here, we focus on building prediction methods using the protein markers for early diagnosis of cancer. To develop a prediction model with high prediction ability, it is critical to choose appropriate markers first. Here, we consider a stepwise selection method using area under the receiver operating characteristic curve (Step-AUC) in order to construct a multi-protein prediction model. We showed that the performance of Step-AUC highly depends on the tuning parameter. We compared our proposed Step-AUC method to stepwise selection using information criteria and support vector machine recursive feature extraction (SVM-RFE). We observed that Step-AUC and stepwise selection using Bayesian information criteria (Step-BIC) perform better than other methods. The importance of each marker can be chosen using a new stepwise selection consistency (SSC) measure. The final models include the markers with high SSC measures. We applied our stepwise procedure to pancreatic cancer data and found two markers of interest. © 2015 IEEE. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Developing cancer prediction model based on stepwise selection by AUC measure for proteomics data | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/BIBM.2015.7359874 | - |
dc.identifier.scopusid | 2-s2.0-84962346084 | - |
dc.identifier.bibliographicCitation | Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, pp 1345 - 1350 | - |
dc.citation.title | Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 | - |
dc.citation.startPage | 1345 | - |
dc.citation.endPage | 1350 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Bioinformatics | - |
dc.subject.keywordPlus | Blood | - |
dc.subject.keywordPlus | Diagnosis | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Information use | - |
dc.subject.keywordPlus | Molecular biology | - |
dc.subject.keywordPlus | Proteins | - |
dc.subject.keywordPlus | Support vector machines | - |
dc.subject.keywordPlus | Area under the curves | - |
dc.subject.keywordPlus | Early diagnosis | - |
dc.subject.keywordPlus | Multiple reaction monitoring | - |
dc.subject.keywordPlus | Protein markers | - |
dc.subject.keywordPlus | Receiver operating characteristic curves | - |
dc.subject.keywordPlus | stepwise selection | - |
dc.subject.keywordPlus | Diseases | - |
dc.subject.keywordAuthor | area under the curve (AUC) | - |
dc.subject.keywordAuthor | early diagnosis of cancer | - |
dc.subject.keywordAuthor | multiple reaction monitoring (MRM) | - |
dc.subject.keywordAuthor | protein marker | - |
dc.subject.keywordAuthor | Receiver operating characteristic (ROC) curve | - |
dc.subject.keywordAuthor | stepwise selection | - |
dc.subject.keywordAuthor | support vector machine | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7359874 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Sookmyung Women's University. Cheongpa-ro 47-gil 100 (Cheongpa-dong 2ga), Yongsan-gu, Seoul, 04310, Korea02-710-9127
Copyright©Sookmyung Women's University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.