{"52783":{"#nid":"52783","#data":{"type":"event","title":"CSE Seminar - Matthew Hibbs","body":[{"value":"\u003Ch3\u003EDr. Matthew Hibbs\u003C\/h3\u003E\n\u003Ch3\u003EDepartment of Computer Science\u003C\/h3\u003E\n\u003Ch3\u003EPrinceton University\u003Cbr \/\u003E\u003C\/h3\u003E\n\u003Cp\u003E\u003C\/p\u003E\n\u003Ch2\u003EAnalysis of large-scale gene expression microarray compendia\u003C\/h2\u003E\n\u003Cp\u003EOver the past decade, gene expression microarray data has become an important tool for biologists to understand molecular processes and mechanisms on the whole-genome scale.\u00a0 Microarray data provides a window into the inner workings of the transcriptional process that is vital for cellular maintenance, development, biological regulation, and disease progression.\u00a0 While a rapidly increasing amount of microarray data is being generated for a wide variety of organisms, there is a severe lack of methods designed to utilize the vast amount of data currently available.\u00a0 In my work, I explore several techniques to meaningfully harness large-scale collections of microarray data both to provide biologists with a greater ability to explore data repositories, and to computationally utilize these repositories to discover novel biology.\u003C\/p\u003E\n\u003Cp\u003EFirst, effective search and analysis techniques are required to guide researchers and enable their effective use of large-scale compendia. I will present a user-driven similarity search algorithm designed to both quickly locate relevant datasets in a collection and to then identify novel players related to the user\u2019s query.\u00a0 Second, I will describe novel methods that allow users to simultaneously visualize multiple datasets with the goal of providing a larger biological context within which to understand these data.\u00a0 Finally, I will discuss how we have used these approaches to discover novel biology, including successfully directing a large-scale experimental investigation of S. cerevisiae mitochondrial organization and biogenesis.\u003C\/p\u003E\n\u003Cp\u003EBio\u003Cbr \/\u003EMatthew Hibbs is a computational biologist working in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University, where he recently earned his PhD under the guidance of Olga Troyanskaya and Kai Li.\u00a0 Matt\u2019s research interests are focused on incorporating expert biological knowledge into the computational analysis of high-throughput genomic data.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":"","uid":"27154","created_gmt":"2010-02-11 15:57:51","changed_gmt":"2016-10-08 01:50:09","author":"Louise Russo","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2008-03-24T12:00:00-04:00","event_time_end":"2008-03-24T13:00:00-04:00","event_time_end_last":"2008-03-24T13:00:00-04:00","gmt_time_start":"2008-03-24 16:00:00","gmt_time_end":"2008-03-24 17:00:00","gmt_time_end_last":"2008-03-24 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"47223","name":"College of Computing"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"David Bader","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}