{"394591":{"#nid":"394591","#data":{"type":"event","title":"PhD Defense by Tonya Woods","body":[{"value":"\u003Cp\u003ETitle: \u0026nbsp;\u003Cstrong\u003EExtracting Meaningful Statistics for the Characterization and Classification of Biological, Medical, and Financial Data\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdvisor:\u0026nbsp; Professor Brani Vidakovic\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ECommittee members:\u0026nbsp; Professor Yajun Mei, Professor Kamran Paynabar, Professor Mirjana Milosevic-Brockett (School of Biology), Dr. Scott Nickleach (Equifax INC)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDate and time:\u0026nbsp; Friday, May 1, 2015, 10:00 AM\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELocation:\u0026nbsp; ISyE Groseclose, Room 226A\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis thesis is focused on extracting meaningful statistics for the characterization and classification of biological, medical, and financial data and contains four chapters.\u0026nbsp; The first chapter contains theoretical background on scaling and wavelets, which supports the work in chapters two and three.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn the second chapter, we outline a methodology for representing sequences of DNA nucleotides as numeric matrices in order to analytically investigate important structural characteristics of DNA.\u0026nbsp; This methodology involves assigning unit vectors to nucleotides, placing the vectors into columns of a matrix, and accumulating across the rows of this matrix.\u0026nbsp; Transcribing the DNA in this way allows us to compute the 2-D wavelet transformation and assess regularity characteristics of the sequence via the slope of the wavelet spectra.\u0026nbsp; In addition to computing a global slope measure for a sequence, we can apply our methodology for overlapping sections of nucleotides to obtain an evolutionary slope.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn the third chapter, we describe various ways wavelet-based scaling may be used for cancer diagnostics.\u0026nbsp; There were nearly half of a million new cases of ovarian, breast, and lung cancer in the United States last year.\u0026nbsp; Breast and lung cancer have highest prevalence, while ovarian cancer has the lowest survival rate of the three.\u0026nbsp; Early detection is critical for all of these diseases, but substantial obstacles to early detection exist in each case.\u0026nbsp; In this work, we use wavelet-based scaling on metabolic data and radiography images in order to produce meaningful features to be used in classifying cases and controls.\u0026nbsp; Computer-aided detection (CAD) algorithms for detecting lung and breast cancer often focus on select features in an image and make a priori assumptions about the nature of a nodule or a mass.\u0026nbsp; In contrast, our approach to analyzing breast and lung images captures information contained in the background tissue of images as well as information about specific features and makes no such a priori assumptions.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn the fourth chapter, we investigate the value of social media data in building commercial default and activity credit models.\u0026nbsp; We use random forest modeling, which has been shown in many instances to achieve better predictive accuracy than logistic regression in modeling credit data.\u0026nbsp; This result is of interest, as some entities are beginning to build credit scores based on this type of publicly available online data alone.\u0026nbsp; Our work has shown that the addition of social media data does not provide any improvement in model accuracy over the bureau only models.\u0026nbsp; However, the social media data on its own does have some limited predictive power.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Extracting Meaningful Statistics for the Characterization and Classification of Biological, Medical, and Financial Data"}],"uid":"27707","created_gmt":"2015-04-08 11:33:33","changed_gmt":"2016-10-08 02:11:35","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2015-05-01T11:00:00-04:00","event_time_end":"2015-05-01T13:00:00-04:00","event_time_end_last":"2015-05-01T13:00:00-04:00","gmt_time_start":"2015-05-01 15:00:00","gmt_time_end":"2015-05-01 17:00:00","gmt_time_end_last":"2015-05-01 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"1366","name":"defense"},{"id":"1808","name":"graduate students"},{"id":"121301","name":"graduate students. defense. PhD."}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}