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A Novel Approach For Cancer Characterization Using Latent Dirichlet Allocation and Disease-Specific Genomic Analysis

Yalamanchili, Hima Bindu
http://rave.ohiolink.edu/etdc/view?acc_num=wright1527600876174758

Abstract Details

2018, Doctor of Philosophy (PhD), Wright State University, Biomedical Sciences PhD.
Two challenging problems in the clinical study of cancer are the characterization of cancer subtypes and the classification of individual patients according to those subtypes. Further, understanding the role of differential gene expression in the development of and molecular response to cancer is a complex problem that remains challenging, in part due to the sheer number of genes and gene products involved. Traditional statistical approaches addressing these problems are hindered by within-class heterogeneity and challenges inherent in data integration across high-dimensional data. In addition, many current machine learning methods do not lend themselves to biological interpretation. We have developed a novel Latent Dirichlet Allocation (LDA)-based classification approach to classify unknown samples based on similarity of co-expression patterns and mitigate these challenges. Integrating this approach with several recently-developed feature engineering and visualization methods, including Disease Specific Genomic Analysis (DSGA) and topological data analysis (TDA), we developed an analysis pipeline that achieves high accuracy compared to state-of-the-art approaches. We demonstrate the effectiveness of this pipeline on several data sets including RNA-Seq data from Illumina HiSeq 2000 for breast cancer and lung cancer identification, mRNA expression data from Agilent Hu25k microarray for breast cancer subtype identification and copy-number data from Affymetrix SNP6.0 for melanoma identification. We also present functional analysis to identify relevant genes and the associated pathways that could potentially be involved in differentiating different tumor types.
Michael Raymer, Ph.D. (Advisor)
Nicholas Reo, Ph.D. (Committee Member)
Travis Doom, Ph.D. (Committee Member)
Michael Markey, Ph.D. (Committee Member)
Gengxin Li, Ph.D. (Committee Member)
110 p.
Bioinformatics; Biomedical Research
cancer; cancer subtypes; cancer characterization; latent dirichlet allocation; disease-specific genomic analysis; genomic analysis

Recommended Citations

Citations

  • Yalamanchili, H. B. (2018). A Novel Approach For Cancer Characterization Using Latent Dirichlet Allocation and Disease-Specific Genomic Analysis [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527600876174758

    APA Style (7th edition)

  • Yalamanchili, Hima Bindu. A Novel Approach For Cancer Characterization Using Latent Dirichlet Allocation and Disease-Specific Genomic Analysis . 2018. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1527600876174758.

    MLA Style (8th edition)

  • Yalamanchili, Hima Bindu. "A Novel Approach For Cancer Characterization Using Latent Dirichlet Allocation and Disease-Specific Genomic Analysis ." Doctoral dissertation, Wright State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527600876174758

    Chicago Manual of Style (17th edition)

wright1527600876174758
613
© 2018, some rights reserved.Creative Commons License A Novel Approach For Cancer Characterization Using Latent Dirichlet Allocation and Disease-Specific Genomic Analysis by Hima Bindu Yalamanchili is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.