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RSG with DREAM & Cytoscape Workshop | November 6 - 9, 2016 | Phoenix, AZ |DREAM Posters


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D# = Cytoscape related work

Authors Title
D1 Istvan Ladunga, Avi Knecht, Adam Caprez, Timothy Bailey, Tao Liu, Pedro Madrigal and David Swanson ChIPathlon: community-wide performance assessment of tools for mapping transcription factor binding sites and histone modifications
D2 Ana Stanescu and Gaurav Pandey Learning parsimonious ensembles for biomedical prediction problems and DREAM challenges
D3 Robert Vogel, Mehmet Eren Ahsen and Gustavo Stolovitzky Unsupervised aggregation of rank predictions for binary classification in translational systems biology tasks
D4 David Tanenbaum Phd, Eldred Ribeiro Phd, Wenling Chang Phd, Peter Gutgarts, Ari Abrams-Kudan, William Kim, Lisa Tutterow, Lauren Quattrochi Phd and Erin Williams Jd Catalyzing Biomedical Research through the NIH Commons Credit Cloud Computing Paradigm
D5 Dennis Wang, Michael P. Menden, Michael Mason, Thomas Yu, Krishna C. Bulusu, Elias Chaibub Neto, In Sock Jang, Zara Ghazoui, John Vincent, Eric Tang, Giovanni Di Veroli, Gustavo Stolovitzky, Jonathan R. Dry, Justin Guinney, Julio Saez-Rodriguez Community analysis to predict therapeutic synergy within the AstraZeneca-Sanger Drug Combination Challenge
D6 Minji Jeon, Sunkyu Kim, Sungjoon Park, Heewon Lee, Hyeokyoon Chang, Minhwan Yu, Kwanghun Choi, and Jaewoo Kang Drug Synergy Prediction using High Performance Computing and Support Vector Regression
D7 Tin Nguyen, Bence Szalai, Gábort Turu, Miklós Cserző, László Hunyady, Sorin Draghici, Russ Wolfinger Boosted Tree Based Prediction of Drug Synergisms in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge
D8 Weijia Zhang, Thuc Le, Taosheng Xu, Junpeng Zhang, Lin Liu, Jiuyong Li Disease module identification with balanced multi-layer regularized Markov cluster algorithm
D9 Chi W Pak Bootstrap feature selection of early differentially expressed genes in human respiratory viral challenge studies: viral shedding and patient symptoms
D10 Maria Suprun, Joel Correa-da-Rossa, Mayte Suarez-Farinas and Lewis E Tomalin Estimating a generalizable ‘final model’, suitable for clinical classification applications, using Monte Carlo resamples of patient data
 D11  Artem Lysenko, Piotr J. Kamola, Keith A. Boroevich and Tatsuhiko Tsunoda  Strategies for discovering disease-associated modules in integrated biological networks
 D12  Emilie Ramsahai and Melford John  M-Reach pathways from networks combined on V-Structures
 D13  Jake Crawford, Junyuan Lin, Xiaozhe Hu, Benjamin Hescott, Donna Slonim and Lenore J. Cowen  A double spectral approach to the DREAM module identification challenge
 D14  Sergio Gómez, Manlio De Domenico and Alex Arenas  Module Identification by Adjusting Resolution in Community Detection Algorithms
 D15 Shana White, Lixia Zhang and Mario Medvedovic  Application of diffusion kernels of network graphs and subsequent clustering approaches for identification of disease modules
D16 Hongjiu Zhang and Yuanfang Guan An ultra-fast tumour heterogeneity inference method using density-based infinite mixture model
D17 Liye He, Eemeli Leppäaho, Motoki Shiga, Bhagwan Yadav, Jussi Gillberg, Suleiman Khan, Zia ur Rehman, David Tamborero, Pekka Marttinen, Mehmet Gönen, Hiroshi Mamitsuka, Krister Wennerberg, Samuel Kaski, Tero Aittokallio, and Jing Tang Target-based ensemble drug combination predictions for the DREAM10 challenge
D18 Eduardo G. Gusmao, Zhijian Li, Ivan G. Costa Prediction of Active Transcription Factor Binding Sites Using Computational Footprinting Data
D19 Andrey S. Lando, Ilya E. Vorontsov, Valentina Boeva, Grigory V. Sapunov, Irina A. Eliseeva, Vsevolod J. Makeev, and Ivan V. Kulakovskiy Preselection of training cell types improves prediction of transcription factor binding sites
D20 Zafer Aydın Discovering Biomarkers for Early Prognosis of Respiratory Virus Infection
D21 Sandeep Kumar Dhanda, Kumardeep Chaudhary, Ritesh ‬Kumar‬, Deepak Singla  ViResPred: A Solution for Respiratory Viral Dream Challenge
D22 Michał Łopuszyński and Miron Bartosz Kursa The application of the feature hashing and the mutual information based feature filter in predicting susceptibility to respiratory viruses


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