Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


12742.pdf (4.56 MB)

ETD Abstract Container

Abstract Header

An Efficient Algorithm for Clustering Genomic Data

Zhou, Xuan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389

Abstract Details

2014, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
In this thesis, we investigated an efficient framework for clustering analysis of gene expression profiles by discretizing continuous genomic data and adopting the 1D-jury approach for fast clustering that was previously used for protein model quality assessment. We demonstrated, through an empirical analysis of multiple data sets from independent studies, that the loss of information due to discretization of genomic data is limited. Patterns observed using the original data can largely be recovered from discretized expression profiles, while enabling efficient identification of genomic signatures and clustering of expression profiles. We further studied the application of 1D-Jury approach in reducing the dimensionality of genomic data. We demonstrated that discretization and 1D-Jury score projection efficiently reduced the dimensionality of feature space. More importantly, the proposed discretization-projection heuristic enhanced the discovery of cluster structure and patterns in the data. Therefore, the proposed discretization-projection method can be a valuable tool for the analysis of gene expression data.
Jaroslaw Meller, Ph.D. (Committee Chair)
Raj Bhatnagar, Ph.D. (Committee Member)
Yizong Cheng, Ph.D. (Committee Member)
70 p.
Computer Science
genomic data; clustering; discretization; 1D-Jury; dimension reduction

Recommended Citations

Citations

  • Zhou, X. (2014). An Efficient Algorithm for Clustering Genomic Data [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389

    APA Style (7th edition)

  • Zhou, Xuan. An Efficient Algorithm for Clustering Genomic Data. 2014. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389.

    MLA Style (8th edition)

  • Zhou, Xuan. "An Efficient Algorithm for Clustering Genomic Data." Master's thesis, University of Cincinnati, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389

    Chicago Manual of Style (17th edition)

ucin1418910389
666
© 2014, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.