Seminars archive
Upcoming Seminars | | No upcoming seminars currently scheduled. | Past Seminars | | Seminar: Numerical Analysis and Scientific Computing Research Spotlights James Nagy and Lars Ruthotto, Emory University | | Defense: Dissertation Efficient Solvers for Nonlinear Problems in Imaging James L Herring, Emory University | | Seminar: Numerical Analysis and Scientific Computing Computational and Predictive Models for Brain Imaging Studies Yi Hong, The University of Georgia | | Colloquium: Computational Mathematics Optimization Methods for Training Neural Networks Jorge Nocedal, Northwestern University | | Seminar: Algebra Lattice Point Counting and Arithmetic Statistics Frank Thorne, University of South Carolina | | Seminar: Combinatorics A proof of a conjecture of Erd\H{o}s et al. about subgraphs of minimum degree k Lisa Sauermann, Stanford University | | Seminar: Combinatorics The maximum number of cycles in a graph Andrii Arman, The University of Manitoba | | Graduate Student Seminar: Computer Science Data Warehousing and Ensemble Learning of Omics Data Xiaobo Sun, Emory University Venue: Room GCR311 of Department of Biostatistics Show abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets where the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically, beyond just metadata to improve “findability”. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. | | Seminar: Algebra Vector-valued Hirzebruch-Zagier series and class number sums Brandon Williams, UC Berkeley | | Colloquium Primes fall for the gambler's fallacy K. Soundararajan, Stanford University |
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