Conference proceeding
Clustering Big Cancer Data by Effect Sizes
2017 IEEE/ACM SECOND INTERNATIONAL CONFERENCE ON CONNECTED HEALTH - APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), pp 58-63
01 Jan 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We propose an effect size based approach to compute initial dissimilarities for Ensemble Algorithm of Clustering Cancer Data (EACCD). The proposed method is applied to the colon cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute and compared with the log-rank approach where initial dissimilarities are computed from the log-rank test statistic. The experimental results show that under the proportional hazards assumption, the effect size approach generates robust results and has a better performance than the log-rank approach.
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Details
- Title
- Clustering Big Cancer Data by Effect Sizes
- Creators
- Huan Wang - George Washington UniversityDechang Chen - Uniformed Services University of the Health SciencesMatthew T. Hueman - Surgical OncologyLi Sheng - Drexel UniversityDonald E. Henson - Uniformed Services University of the Health SciencesIEEE
- Publication Details
- 2017 IEEE/ACM SECOND INTERNATIONAL CONFERENCE ON CONNECTED HEALTH - APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), pp 58-63
- Conference
- 2017 IEEE/ACM 2ND INTERNATIONAL CONFERENCE ON CONNECTED HEALTH - APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), 2nd
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- grant "Using Dendro-grams to Create Prognostic Systems for Cancer - John P. Murtha Cancer Center Research Program"
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000426965000010
- Scopus ID
- 2-s2.0-85029385678
- Other Identifier
- 991019168384504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical