PPT-gSparsify: Graph Motif Based

Sparsification for Graph Clustering Peixiang Zhao Department of Computer Science Florida State University zhaocsfsuedu Synopsis Introduction gSparsify Graph motif based sparsification Cluster significance. What’s a motif paper?. A motif paper allows you to focus on an aspect of a short story, play, or novel. . By exploring a single motif, you are able to draw conclusions and offer insight into the motivation or message of a particular piece of literature.. Simon Andrews. simon.andrews@babraham.ac.uk. @. simon_andrews. v. 1.0. 1. Rationale. 2. Gene A. Gene B. Gene C. Hit A. Hit B. Hit C. Prom A. Prom B. Prom C. GGATCC. GGATCC. GGATCC. Basic Questions. Does the sequence around my hits look unusual?. 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 . Zhizhuo. Zhang . Outline. Review of Mixture Model and EM algorithm. Importance Sampling. Re-sampling EM. Extending EM. Integrate Other Features. Result. Review Motif Finding: Mixture modeling. Given a dataset . 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. 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. Three Important Devices: Know These. Symbol: An object that represents something more significant that just itself. Motif: A . recurring. element or idea; a phrase; image; repetition of similar symbols; repetition of an issue/attitude. 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. Cody Dunne and Ben Shneiderman. {cdunne, ben}@. cs.umd.edu. 30. th. . Annual . Human-Computer Interaction Lab Symposium, May 22–23, 2013. College Park, . MD. Who Uses Network Analysis. Network Analysis is Hard. simon.andrews@babraham.ac.uk. @. simon_andrews. v. 1.0. 1. Rationale. 2. Gene A. Gene B. Gene C. Hit A. Hit B. Hit C. Prom A. Prom B. Prom C. GGATCC. GGATCC. GGATCC. Basic Questions. Does the sequence around my hits look unusual?. Prof. William Stafford Noble. Stuff folks liked. Good pace. x3. Powerpoint. was easy to follow. x2. Really appreciate reading the program step by step. x2. Helpful to go over the sample problems at the beginning of class.. 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. Motif 4. Motif 4. Motif 5. Motif 6. Supplementary . Figure . S2. . Amino acid sequence alignment of the five members of the Arabidopsis PAR1-family proteins (LAT1-5) shows a number of conserved regions and motifs throughout the protein sequence. Motifs were identified using Multiple .