DREAM POSTERS

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D# = Cytoscape related work

Poster Number	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