PPT-1 Joint analysis of regulatory networks and expression profiles

Ron Shamir School of Computer Science Tel Aviv University April 2013 1 Sources Igor Ulitsky and Ron Shamir Identification of Functional Modules using Network Topology and HighThroughput Data BMC Systems Biology 18 2007 . Borodovsky. . a short intro . Position open:. . Scientist - Pathway Informatics (June 2009). THE POSITION . The successful candidate will join the Computational Science & Bioinformatics team. Mountain. The economic regulation of electricity networks in Australia: commentary on a failed reform. Melbourne Economic Forum. 20 November 2014. 2. Source: ABS, CME analysis. Average household prices are comparable though network/non-network mix differs. and Lucia . Ramirez. Loyola Marymount University . March 24, 2015. BIOL398-04: Biomathematical Modeling. Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., Zeitlinger, J., Jennings, E. G., Murray, H.L ., Gordon, D. B., Ren, B., Wyrick, J. J., Tagne, J. B., Volkert, T. L., Fraenkel, E., Gifford, D. K. & Young, R. A. (2002). Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298(5594), 799-804. DOI: 10.1126/science.1075090. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://. compnetbiocourse.discovery.wisc.edu. Sep 27. th. 2016. Teachable Unit. Gene Expression Team. NANSI 2013, University of Minnesota. Participants: Michael Burns, Lucy He, David Kirkpatrick, Bridget Lear, Tamar Resnick, Turk Rhen. Facilitators: Judy Ridgway, Sue Wick. OMICS 5. th. International Pharmaceutical Regulatory Affairs , 3 - 5 August . 2015, Orlando, Florida, USA. Presented by: Mariam . Aslam. Agenda. Introduction. Definition of Regulatory Intelligence (RI). By . Gianluca. . Stringhini. , . Christopher . Kruegel. and Giovanni . Vigna. Presented By Awrad Mohammed Ali. 1. Introduction.. The Popular Social Networks.. Data Collection.. Data Analysis.. Spam Bots Analysis.. in Development, Evolution and History. Manfred D. Laubichler. Arizona State University. Santa Fe Institute. Marine Biological . Laboratory. Max Planck Institute for the History of Science. John’s Challenge for Future Work:. eQTLs. Chris Cotsapas. cotsapas@broadinstitute.org. 6.047/6.878/HST.507. Computational Biology: Genomes, Networks, Evolution. Module 4: Population / Evolution / Phylogeny. L15/16: Association mapping for disease and molecular traits. Outline. Introduction. Two review papers. Quality control (. MetaQC. ). Meta-analysis for detecting differentially expressed genes (. MetaDE. ). Meta-analysis for detecting pathways (. MetaPath. ). 1. Introduction. RNA-Seq. G-OnRamp Beta Users Workshop. Wilson Leung. 07/2017. Outline. Design considerations for RNA-Seq experiments. Interpret FastQC results. Optimize alignment parameters for HISAT. Assess alignment statistics with CollectRnaSeqMetrics. to . regulatory and interaction networks. October 23. rd. . – 25. th. , 2015. HSE – Nizhny Novgorod. Mario R. Guarracino. joint work with Marina . Piccirillo. , . Sonali. . Chavan. , . Parijat. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. https://compnetbiocourse.discovery.wisc.edu. Sep 27. th. 2018. Plan for this section. Overview of network inference (Sep 18. . Transciptional. Response to Cold Shock in . Saccharomyces . cerevisiae. . using . GRNmap. K. Grace Johnson. 1. , Natalie E. Williams. 2. , Margaret J. O’Neil. 2. , . Kam. D. Dahlquist. 2. , and Ben G. Fitzpatrick.