CI-EN: Collaborative Research: Enhancement of Foldit, a Community Infrastructure Supporting Research on Knowledge Discovery Via Crowdsourcing in Computational Biology
CI-EN: Collaborative Research: Enhancement of Foldit, a Community Infrastructure Supporting Research on Knowledge Discovery Via Crowdsourcing in Computational Biology
批准号:
1629879
负责人:
Seth Cooper
金额:
$22.99万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-08-15 至 2019-07-31
中文摘要
点击翻译按钮获取中文摘要
英文摘要
The focus for this project is the combination of crowdsourcing and computational biochemistry through the crowdsourcing video game Foldit. Foldit is an online multiplayer game that allows players to compete and collaborate to computationally fold and design proteins. Since its launch in 2008, it has had over 400,000 players, and demonstrated that by leveraging human problem solving and creativity, humans and computers can work together to solve previously unsolvable problems in computational structural biology. Foldit players have contributed toward solutions for two of the "holy grail" problems in computational structural biology: the protein folding problem and the protein design problem (also known as the inverse folding problem). The design of novel synthetic proteins is one of the most important tools in protein engineering, possibly leading us to a better understanding of the processes that underlie life and allowing the discovery of molecules with applications in health, energy, materials, and nanotechnology. As a new approach to addressing the challenges of purely computational approaches in biochemistry, the field of scientific discovery games has recently arisen to crowdsource solutions to computational biochemistry problems, by using human problem solving and creativity from members of the general public. The infrastructure supported by this project will enable new research in crowdsourcing and citizen science. The broader community will benefit from this infrastructure as a platform for crowdsourcing computational biochemistry.This project aims to enhance the existing infrastructure of the Foldit game, and allow us to recast Foldit to tackle the next big challenges in computational structural biology (including design of enzymes and small molecules), while broadening the community of scientists involved and continuing to engage and educate the public. This project will support equipment and personnel needs for cloud hosting and maintenance, allowing improved robustness and collaborative access to resources, along with continued community outreach. Additionally, support will be provided for developing in-game tools for new applications in protein and small molecule design, new interfaces for scientists, touchscreen and multi-touch support, and a new pipeline for crowdsourced biochemistry challenges. Enhancements made to the game client will be made available via Foldit Standalone -- a non-competitive version of the game that allows researchers to load in and work on their own structures. Educators will be able to use the game as a teaching tool for introducing biochemistry to classrooms, where it is already being used for lectures, lab exercises, homework assignments, and even in textbooks and MOOCs. Foldit has been used by winning student teams in iGEM -- the premiere international design competition for students in synthetic biology. The Foldit website (http://fold.it/) provides access to the online multiplayer game and resources for educators and researchers.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
A Monte Carlo Approach to Skill-Based Automated Playtesting.
基于技能的自动化游戏测试的蒙特卡罗方法。
DOI:
--
发表时间:
2018
期刊:
Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference
影响因子:
--
作者:
[Horn,Britton, Miller,JoshAaron, Smith,Gillian, Cooper,Seth]
通讯作者:
Cooper,Seth
Inferring and Comparing Game Difficulty Curves using Player-vs-Level Match Data.
使用玩家与级别的匹配数据推断和比较游戏难度曲线。
DOI:
10.1109/cig.2019.8848102
发表时间:
2019
期刊:
IEEE Conference on Games 2019 : London, United Kingdom, 20-23 August 2019
影响因子:
--
作者:
[Sarkar,Anurag, Cooper,Seth]
通讯作者:
Cooper,Seth
CHS: Small: Data-Driven Retention in Crowdsourced Image Analysis and Mapping
-
批准号:1816426
-
项目类别:Standard Grant
-
资助金额:$49.49万
-
财政年份:2018
-
负责人:Seth Cooper
-
依托单位:
CAREER: Continual Automated Refinement of Human Computation Systems
-
批准号:1652537
-
项目类别:Continuing Grant
-
资助金额:$54.68万
-
财政年份:2017
-
负责人:Seth Cooper
-
依托单位:
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