Full Record

New Search | Similar Records

Author
Title Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health.
URL
Publication Date
Date Accessioned
Degree PhD
Discipline/Department Bioinformatics
Degree Level doctoral
University/Publisher University of Michigan
Abstract This dissertation develops methods of integrative statistical learning to studies of two human diseases - respiratory infectious diseases and leukemia. It concerns integrating statistically principled approaches to connect data with knowledge for improved understanding of diseases. A wide spectrum of temporal and high-dimensional biological and medical datasets were considered. The first question studied in this thesis examined host responses to viral insult. In a human challenge study, eight transcriptional response patterns were identified in hosts experimentally challenged with influenza H3N2/Wisconsin viruses. These patterns are highly correlated with and predictive of symptoms. A non-passive asymptomatic state was revealed and associated with subclinical infections. The findings were validated and extended to three additional viral pathogens (influenza H1N1, Rhinovirus, and RSV). Their differences and similarities were compared and contrasted. Statistical models were developed for exposure detection and risk stratification. Experimental validations have been performed by collaborators at the Duke University. The second question studied in this thesis investigated the regulatory roles of Hoxa9 and Meis1 in hematopoiesis and leukemia. Methods were developed to characterize their global in vivo binding patterns and to identify their functional cofactors and collaborators. The combinatorial effects of these factors were modeled and related to specific epigenetic signatures. A new biological model was proposed to explain their synergistic functions in leukemic transformation. Experimental validations have been performed by members of the Hess laboratory. Motivated by problems encountered in these studies, two algorithms were developed to identify spatial and temporal patterns from high-throughput data. The first method determines temporal relationships between gene pathways during disease progression. It performs spectral analysis on graph Laplacian-embedded significance measures of pathway activity. The second algorithm proposes probabilistic modeling of protein binding events. Based on information geometry theory, it applies hypothesis testing coupled with jackknife-bias correction to characterize protein-protein interactions. Experimental validations were shown for both algorithms. In conclusion, this dissertation addressed issues in the design of statistical methods to identify characteristic and predictive features of human diseases. It demonstrated the effectiveness of integrating simple techniques in bioinformatics analysis. Several bioinformatics tools were developed to facilitate the analysis of high-dimensional time-series datasets.
Subjects/Keywords Integrative Statistical Learning in High-dimensional Time-series Data; Host Transcriptional Responses to Respiratory Viral Pathogens; Role of Hoxa9 in Leukemic Transformation; Spectral Analysis of Temporal Pathway Activity Using Graph Lapalacian; Information Geometric Analysis of Motif Profiles in ChIP-sequencing; Predictive Modeling and Classification in High-dimensional and Temporal Data; Biomedical Engineering; Genetics; Microbiology and Immunology; Pathology; Science (General); Statistics and Numeric Data; Health Sciences; Science
Contributors Hero Iii, Alfred O. (committee member); Hess, Jay L. (committee member); Burns Jr., Daniel M. (committee member); Omenn, Gilbert S. (committee member); Shedden, Kerby A. (committee member)
Language en
Rights Unrestricted
Country of Publication us
Record ID handle:2027.42/84435
Repository umich
Date Retrieved
Date Indexed 2020-09-09
Grantor University of Michigan, Horace H. Rackham School of Graduate Studies
Issued Date 2011-01-01 00:00:00
Note [thesisdegreename] Ph.D.; [thesisdegreediscipline] Bioinformatics; [thesisdegreegrantor] University of Michigan, Horace H. Rackham School of Graduate Studies; [bitstreamurl] http://deepblue.lib.umich.edu/bitstream/2027.42/84435/1/huangys_1.pdf;

Sample Search Hits | Sample Images

…simplicity. We show how simple statistical modeling techniques are effective in deriving and integrating knowledge from complex biomedical data without over-complicating the model. – An spectral method for studying temporal disease dynamics. In Chapter V, we…

…4.4.2 Experimental Procedure . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 137 139 141 142 145 145 146 150 V. Spectral Analysis Of Temporal Gene Pathway Activation During Influenza Virus-induced…

…statistically principled approaches to connect data with knowledge for improved understanding of diseases. A wide spectrum of temporal and high-dimensional biological and medical datasets were considered. The first question studied in this thesis examined host…

…Motivated by problems encountered in these studies, two algorithms were developed to identify spatial and temporal patterns from high-throughput data. The first method determines temporal relationships between gene pathways during disease progression. It…

…analyzing protein binding pattern in whole-genome sequencing studies. Existing methodologies on sequence motif analysis are reviewed in order to provide a context for discussion. Chapter V describes a spectral analysis method for studying temporal activities…

…develop an algorithm to perform spectral analysis of temporal gene pathway activities by embedding of their statistical significance measures of with graph 6 Laplacian. – An information geometry-based method for inferring protein-protein interactions. In…

…Host Response in Symptomatic Respiratory Viral Infection. In preparation for submission in 2011. Y. Huang, A. Rao, A. Hero III. Spectral Analysis of Temporal Gene Pathway Activities. In preparation for submission in 2011. Y. Huang, G. Robertson, J…

…In preparation for submission in 2011. Software Implementations cMotif: ChIP-sequencing motif analysis utilities. SpecPath: Spectral analysis of temporal pathway activities. roMA2C: An object-oriented multi-array ChIP-chip analysis toolkit. rBLU: an R…

.