DREAM Community

Author Archive | dreamadmin

December 6, 2016

DREAM 11- 2016 All-Stars

AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge

Sub-Challenge 1A

Best Performer:
Team Guan Lab – Yuanfang Guan, University of Michigan (USA)

Tied 2nd Place:
Team: North Atlantic
az-northatlantic

Russ Wolfinger, SAS Institute Inc. (USA); Tin Nguyen & Sorin Draghici, Advaita Corporation and Wayne State University (USA); and Bence Szalai, Gábor Turu, Miklós Cserző, & László Hunyady: Semmelweis University, and MTA-SE Laboratory of Molecular Physiology (Hungary)

Team: Mikhail Zaslavskiy
Mikhail Zaslavskiy

Sub-challenge 1B

Best Performer:
Team Guan Lab – Yuanfang Guan, University of Michigan (USA)

Tied 2nd Place:
Team: DMIS
az-teamdimsjpg

Jaewoo Kang, Minji Jeon, Sunkyu Kim, Sungjoon Park, Heewon Lee, Hyeokyoon Chang, Minhwan Yu, and Kwanghun Choi, Korea University, (Korea)

Team: North Atlantic
Russ Wolfinger, SAS Institute Inc. (USA); Tin Nguyen & Sorin Draghici, Advaita Corporation and Wayne State University (USA); and Bence Szalai, Gábor Turu, Miklós Cserző, & László Hunyady: Semmelweis University, and MTA-SE Laboratory of Molecular Physiology (Hungary)

Team Puruguraming
Amin Allam, KAUST (Saudi Arabia)

Sub-challenge 2

Best Performer:
Team Guan Lab – Yuanfang Guan, University of Michigan (USA)

Team: North Atlantic
Russ Wolfinger, SAS Institute Inc. (USA); Tin Nguyen & Sorin Draghici, Advaita Corporation and Wayne State University (USA); and Bence Szalai, Gábor Turu, Miklós Cserző, & László Hunyady: Semmelweis University, and MTA-SE Laboratory of Molecular Physiology (Hungary)

 Disease Module Identification DREAM Challenge –

Sub-Challenge 1

Best Performer:
Team Tusk:
disease_module-tusk

Jake Crawford, Junyuan Lin, Xiaozhe Hu, Benjamin Hescott, Donna Slonim, & Lenore Cowen; Tufts University (USA)

Honorable Mentions:

Team Aleph:
disease_module-aleph
Sergio Gómez, Manlio De Domenico, Alex Arenas.

Team: Causality:
Weijia Zhang, Thuc Le, Taosheng Xu, Junpeng Zhang, Lin Liu, Jiuyong Li.

Team: PureMichigan:
Minjun Kim, Yuanfang Guan, University of Michigan (USA).

Team: ShanHeLab:
Dong Li, Benjapun Kaveelerdpotjana, Jiarui Zhou, Ning Shi, Weiqi Chen, Shan He.

Team: TeamDMIS:
Sungjoon Park, Wonho Shin, Minji Jeon, Sunwon Lee, Jaewoo Kang, Korea University, (Korea)

Team: Tianle Ma:
Tianle Ma.

Team: TripleAHC:
Hatem Hamza, Anton Heijs, Antonis Loizou, Andra Waagmeester, Christine Chichester.

Sub-Challenge 2

Team Tsurumi-Ono:
disease_module-tsunami
Artem Lysenko, Piotr J. Kamola, Keith A. Boroevich, & Tatsuhiko Tsunoda; RIKEN Center for Integrative Medical Sciences (Japan)

ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge

Conference Round

Team: autosome.ru
encode_autosome
Vsevolod Makeev, Andrey Lando, Grigory Sapunov, Ilya Vorontsov, Ivan Kulakovskiy, Valentina Boeva, & Irina Eliseeva; Russian Academy of Sciences; Moscow Institute of Physics and Technology; and Intento, (Russia) and Institut Cochin (France)

Team: J-Team
encode-j-team
Jens Keilwagen, Stefan Posch, & Jan Grau; Federal Research Centre for Cultivated Plants; and Martin Luther University Halle-Wittenberg  (Germany)

Team HINT
encode-hintjpg
Eduardo G. Gusmao, Zhijian Li, and  Ivan G. Costa; Aachen University Medical School  and RWTH Aachen University(Germany) Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health (USA)

ICGC-TCGA-DREAM Somatic Mutation Calling Challenge- Tumor Heterogeneity and Evolution (SMC-HET)

Team: GISL
smchet_gisl
Kaiyi Zhu, Tai-Hsien Ou Yang and Dimitris Anastassiou; Columbia University (USA)

Team: GuanLab
smchet_guan-lab-pg
Hongjiu Zhang and Yuanfang Guan; University of Michigan, (USA)

July 4, 2015

Daniel Marbach

Daniel MarbachIn the first edition of the DREAM challenges in 2007, I was a participant and best performer. Subsequently, I designed and led three editions of the DREAM network inference challenge (2008, 2009, 2010). Currently, I’m co-leading the DREAM gene essentiality prediction challenge. My involvement with DREAM has been a central part of my PhD thesis and postdoctoral work. I will continue my engagement with DREAM in the future to help promote open and collaborative science through crowdsourced challenges. Through my involvement with DREAM I changed my research focus from trying to show that my own methods work best, towards unbiased method assessment and creation of useful resources for the community. This work led to several highly cited papers that helped advance my academic career.

November 6, 2014

2014 Publications

James Costello , Heiser L, Georgii E, et al.
A community effort to assess and improve drug sensitivity prediction algorithms;
Nature Biotechnology (2014) doi:10.1038/nbt.2877

Robert Küffner , Zach N, Norel R, Hawe J, et al,
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression,
Nature Biotechnology (2014) doi:10.1038/nbt.3051

Meyer, Pablo;  Cokelaer, Thomas;  Chandran, Deepak;  Kim, Kyung Hyuk;  Loh, Po-Ru et al.
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
BMC Systems Biology vol. 8 (1) p. 13 (2014) doi:10.1186/1752-0509-8-13

Mukesh Bansal, et. al;
A community computational challenge to predict the activity of pairs of compounds
Nature Biotechnology,(2014) doi:10.1038/nbt.3052

October 22, 2014

Kevin Yip

When I participated in the DREAM3 in silico network reconstruction challenge, I had very little experience in reconstructing TF-regulatory networks (I was focusing on protein-protein interaction networks at that time). Given the many sophisticated methods existing at that time, I just hoped to participate and get some experience in this topic. However, perhaps because I was new to this topic, my method was apparently quite different from the other methods, and it seemed to put more emphasis on a new type of features (expression change due to perturbation) than the other methods, which turned out to be very useful. It has helped me to always ask if there are better features to use or better ways to use the existing features before drilling deep into improving subtle details of existing methods. I think this experience has guided my way to dealing with new problems.

October 22, 2014

Carlin Daniel

DREAM competitions are great because they instantly establish the credibility of computational methods. It is often difficult to discern in outfield whether a method has been over-trained for an particular application. By separating the analysts from the evaluation, we can mitigate the risk, and gain more insight into the general applicability of new methods.   Personally, I received instant positive feedback as a senior graduate student on a winning team in the form of invitations to interview for post-doc positions. Thanks DREAM!

October 20, 2014

Yon D Chang

DREAM challenge is the Olympic arena in this field. It is a great way to communicate with experts and learn state-of-art techniques, and to realize the pros and cons.

October 19, 2014

A DREAM Participant from 2008, 2009, 2010 & 2013

The DREAM challenges have provided a platform to understand network inference algorithm behaviors and experimental data characteristics, unlikely to be as productive if done by individual researchers or labs. This provides an opportunity to evaluate ones work more objectively, in addition to the peer-review system.

DREAM Challenges have greatly influenced the research activities in our lab. We studied the performance of our algorithms in DREAM3,4,5 for their inherent limitations. This led to the design of a novel algorithm in 2010 now called FunChisq that claimed a best performer in DREAM8 network inference challenge. Without DREAM challenges, it would have been unlikely for us to invent the algorithm and even more unlikely to appreciate its power on diverse experimental data.

October 19, 2014

Liu Z, Zhang XS, Zhang S

DREAM has expanded my understanding on several topic which I am very interested in. I cite the DREAM in my lecture and show our related work with it. We have participated in several Challenges. We have enjoyed the competition with so many researchers from different places. We not only conducted the Challenge projects, but also sometimes formulated our own problem or thout the open Challenge problem in a different way. An example is that we participate the Sage Bionetworks – DREAM Breast Cancer Prognosis Challenge. However, we didn’t get ideal or expected prediction results, which enable us to think the prediction ability issue on survival using molecular profiling data and how cancer heterogeneity affect the prediction. Surprisingly, we got several new findings which was published in a recent paper.     Liu Z, Zhang XS, Zhang S. Breast tumor subgroups reveal diverse clinical prognostic power. Sci Rep. 2014, 4:4002.