PPT-A Graph-Based

Author : jane-oiler | Published Date : 2016-03-25

Recovery and Decomposition of Swansons Hypothesis using Semantic Predications Delroy Cameron Olivier Bodenreider Hima Yalamanchili Tu Danh Sreeram Vallabhaneni

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A Graph-Based: Transcript

Recovery and Decomposition of Swansons Hypothesis using Semantic Predications Delroy Cameron Olivier Bodenreider Hima Yalamanchili Tu Danh Sreeram Vallabhaneni. Wei Wang. Department of Computer Science. Scalable Analytics Institute. UCLA. weiwang@cs.ucla.edu. Graphs/Networks. FFSM (ICDM03), SPIN (KDD04),. GDIndex. (ICDE07). MotifMining. (PSB04, RECOMB04, ProteinScience06, SSDBM07, BIBM08). Graphs are a flexible & unifying model. Scalable similarity searches through novel index structure. Mining of significant fragments in collections. Classification of compounds based on significant fragments . Arijit. Khan, Nan Li, . Xifeng. Yan, . Ziyu. Guan. Computer Science . UC Santa Barbara. {. arijitkhan. , . nanli. , . xyan. , . ziyuguan. }@. cs.ucsb.edu. . Supriyo. Chakraborty. UC Los Angeles. Sparsification for Graph Clustering. Peixiang Zhao. Department of Computer Science. Florida State University. zhao@cs.fsu.edu. Synopsis. Introduction. gSparsify. : Graph motif based sparsification. Cluster significance. Arend Rensink, University of Twente. CamPaM 2012. April 2012. Graph-Based State Spaces. April 2012. Graph-Based State Spaces. 2. Graph Transformation. Formal language to capture dynamic system behaviour. Towards Practical Verifiable Graph Processing. Yupeng. Zhang, . Charalampos. . Papamanthou. and . Jonathan . Katz. University of . Maryland. Agenda. Background of verifiable computation and motivation. Richard Peng. Georgia Tech. Based on . recent works . joint with:. Serban . Stan (Yale. ), . Haoran. . Xu (MIT. ),. Shen . Chen Xu (CMU. ), . Saurabh. . Sawlani. (. GaTech. ). John . Gilbert (UCSB. William Cohen. 1. Announcements. Next Tuesday 12/8:. Presentations for 10-805 projects.. 15 minutes per project.. Final written reports due Tues 12/. 15. For exam:. S. pectral clustering will not be cov. Richard Peng. Georgia Tech. Based on . recent works . joint with:. Serban . Stan (Yale. ), . Haoran. . Xu (MIT. ),. Shen . Chen Xu (CMU. ), . Saurabh. . Sawlani. (. GaTech. ). John . Gilbert (UCSB. William Cohen. 1. Announcements. Next Tuesday 12/8:. Presentations for 10-805 projects.. 15 minutes per. project.. Final written reports due Tues 12/15. 2. Graph-Based Parallel Computing. William Cohen. William Cohen. 1. Computing paradigms. Stream-and-sort. Iterative streaming ML (. eg. SGD. ). with . minibatch. vectorization and GPUs. Map-reduce (stream-and-sort parallelism). plus dataflow-language abstractions. Arijit. Khan, Nan Li, . Xifeng. Yan, . Ziyu. Guan. Computer Science . UC Santa Barbara. {. arijitkhan. , . nanli. , . xyan. , . ziyuguan. }@. cs.ucsb.edu. . Supriyo. Chakraborty. UC Los Angeles. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. https://compnetbiocourse.discovery.wisc.edu. Nov 20. th. 2018. RECAP of problems in network biology. Computer science 2. nd. Stage . College of Pharmacy. Hussein Alsheakh, Ph.D.. 1. Use the right type of graphic. Charts and graphs. Bar chart. : comparisons, categories of data. Histogram. : represents relative frequency of continuous data.

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