{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Statistical Frameworks for Integrative Analysis of Genetic Data","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/77466"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"We studied three major interconnected projects focused on establishing statistical frameworks for integrative analysis of genetic data, based on Cox proportional hazard models, kernel Cox regressions, multiple kernel learning models, regularized regression models, and support vector machines regressions, incorporated with dimensionality reduction and feature selections. (1) Our first project employed several machine learning algorithms for clinical predictions utilizing omics data across tumor types, to explore the potential benefits of including genetic measurements with traditional clinical information, for supporting doctor decisions. To predict survival of patients with tumors, our study focused on two objectives. First we applied multivariate Cox proportional hazard (Cox) models with univariate Cox screen or correlation screen, plus L1 penalized log partial likelihood (LASSO) for feature selection. Second, we also examined the factors that could affect prediction of dichotomized survival data by different machine learning algorithms, especially the MKL algorithms for it's capable of data fusion. Our analysis results indicate that incorporating Omics data with clinical information, can significantly improve predictions. Our also provided a well-established frameworks and resources, for reliable prognostic modeling and therapeutic decision making. (2) Our second project involved comprehensively assessing, by using genome-wide DNA methylation data as markers, the contribution of epigenetic effects on asthma and blood related quantitative traits. To evaluate the clinical utility of epigenetic markers, we constructed and compared various prediction models by including top ranked methylation loci from the genome-wide association scan, together with selected sets of known genetic markers from published genome-wide association studies. We observed a significant increase of correlation coefficient between actual and predicted IgE level when methylation markers were included. We also assessed the performance of cross platform prediction using methylation markers. Taken together, results from our assessment suggest that methylation has great potential in prediction of clinical phenotype. (3) Our third project explored kernel Cox regression models to improve the prediction accuracy of patients with metastatic castrate resistant prostate cancer (mCRPC) that treated by docetaxel. We proposed a future direction of utilizing clinical kernels in kernel Cox regression, to potentially obtain better results than linear and Gaussian kernels, with clinical variables only for prognostic modeling."},{"label":"dcterms.available","value":"2017-09-20T16:52:45Z"},{"label":"dcterms.contributor","value":"Wang, Xuefeng"},{"label":"dcterms.creator","value":"Peng, Lizhen"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:45Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:45Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics."},{"label":"dcterms.extent","value":"92 pg."},{"label":"dcterms.format","value":"Application/PDF"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77466"},{"label":"dcterms.issued","value":"2015-05-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:45Z (GMT). No. of bitstreams: 1\nPeng_grad.sunysb_0771E_12343.pdf: 2648730 bytes, checksum: a5b4212e7b6bd890bc261b0f866aa7b4 (MD5)\n  Previous issue date: 2015"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Integrative Analysis, Statistical Frameworks"},{"label":"dcterms.title","value":"Statistical Frameworks for Integrative Analysis of Genetic Data"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/13%2F20%2F29%2F132029699153855388337474330616946258359/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/13%2F20%2F29%2F132029699153855388337474330616946258359","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}