Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


dissertation_chao_wang.pdf (4.71 MB)

ETD Abstract Container

Abstract Header

Integrative Analysis of Multi-modality Data in Cancer

Wang, Chao
http://rave.ohiolink.edu/etdc/view?acc_num=osu1429791373

Abstract Details

2015, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Gleaning insights of highly complex, heterogeneous cancer biology requires data collected from different levels - genetic, genomic and phenotypic. There is a high degree of diversity between individuals with a wide spectrum of clinical, pathologic, and molecular features. Traditionally in clinical settings, phenotypic data such as histopathological images are often used for diagnosis, subtyping, staging, prognosis and treatment. With the advent of new high-throughput biotechnologies, multi-modality of genomics and genetic data provide extremely valuable information for cancer research and clinical biomarker discovery. However, the challenge still remains towards the determination of causal relationship in these multi-modality data and effective integration to gain better understanding of cancer biology. In particular, molecular basis of cellular phenotypes manifest in histopathological images are unknown and remain inexplicable. In this dissertation, I present a new analytic framework and accompanying computational methods to facilitate integrative analyses of multi-modality biomedical data. The first part of this volume describes the extraction of image features thus enabling quantitative analysis of the cellular structures. Our feature collections include texture features, previously discovered salient features and features designed to mimic the observations of a trained pathologist. In the next part, studies that establish the genotype–phenotype links using morphological features from histopathology are presented. Molecules and molecular events associated with breast cancer morphology are discovered. In the third part, beyond pairwise correlations, I explore multivariate molecular basis of lung adenocarcinoma morphology. This study suggests that a cellular structure can be potential target in treatment of lung adenocarcinoma. Finally, the last part aims to develop computational methods that can jointly cluster cancer patient samples based on multi-modality data. These effective integrative cluster methods allow patient stratification based on both essential categorical attributes and multi-dimensional data from different sources. I demonstrate the application of these methods using datasets pertaining to breast cancer. The proposed image processing workflows, the collection of morphological features, the analytical framework that links molecular expression to morphological measurements, and the integrative clustering methods show potential in revealing biological basis and new therapeutic targets of various types of cancer. The results from the studies indicate biologically interesting subtypes with potential biomarkers. The frameworks and methodologies presented in this dissertation can mine the large and complex collections of data to identify new comprehensive biomarkers generate new hypothesis.
Kun Huang (Advisor)
Raghu Machiraju (Committee Co-Chair)
Umit Catalyurek (Committee Member)
Charles Shapiro (Committee Member)
Lori Dalton (Committee Member)
202 p.
Bioinformatics; Computer Engineering

Recommended Citations

Citations

  • Wang, C. (2015). Integrative Analysis of Multi-modality Data in Cancer [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429791373

    APA Style (7th edition)

  • Wang, Chao. Integrative Analysis of Multi-modality Data in Cancer. 2015. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1429791373.

    MLA Style (8th edition)

  • Wang, Chao. "Integrative Analysis of Multi-modality Data in Cancer." Doctoral dissertation, Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429791373

    Chicago Manual of Style (17th edition)

osu1429791373
811
© 2015, some rights reserved.Creative Commons License Integrative Analysis of Multi-modality Data in Cancer by Chao Wang 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 The Ohio State University and OhioLINK.