000 04939nam a2200553 i 4500
001 011452625848
005 20260528133338.0
006 m eo d
007 cr cn |||m|||a
008 150428s2014 nyua fob 000 0deng d
020 _a9781627055055
_qebook
020 _z9781627055062
_qePub
020 _z9781627055048
_qprint
024 7 _a10.1145/2625848
_2doi
035 _a(OCoLC)908155772
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aGV1469.34.S3
_bC664 2014
082 0 4 _a794.8
_223
035 _a(CaBNVSL)swl00404866
100 1 _aCooper, Seth,
_d1982-,
_eauthor.
245 1 2 _aA framework for scientific discovery through video games /
_cSeth Cooper.
250 _aFirst edition.
264 1 _a[New York] :
_bAssociation for Computing Machinery ;
_a[San Rafael, California] :
_bMorgan & Claypool,
_c2014.
300 _a1 PDF (xiv, 117 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aACM books ;
_v#3
504 _aIncludes bibliographical references (pages 111-117).
506 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
588 _aTitle from PDF title page (viewed on April 28, 2015).
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
505 0 _a1. Introduction -- 1.1 Motivation -- 1.2 Problem statement -- 1.2.1 Game design problem -- 1.2.2 Biochemistry discovery problem -- 1.3 Outline --
505 8 _a2. Related literature -- 2.1 Volunteer computing and human computation -- 2.2 Serious games and gamification -- 2.3 Computational biochemistry --
505 8 _a3. Framework -- 3.1 Introduction -- 3.2 Biochemistry background -- 3.3 Framework description -- 3.3.1 Architecture -- 3.3.2 Coevolution strategy -- 3.3.3 Categorization as a game -- 3.4 Game design challenges -- 3.4.1 Visualizations -- 3.4.2 Interactions -- 3.4.3 Scoring -- 3.4.4 Introductory levels -- 3.5 Rewards and social interaction -- 3.5.1 Rewards and ranking types -- 3.6 Conclusion --
505 8 _a4. Protein structure prediction -- 4.1 Introduction -- 4.2 Quest to the natives -- 4.3 CASP8 experiments -- 4.4 Evaluation -- 4.5 Rebuild and refine comparison -- 4.5.1 First strand swap example -- 4.5.2 Player contribution and expertise -- 4.6 Alignment tool and CASP9 -- 4.7 Solution of crystal structure -- 4.8 Conclusion --
505 8 _a5. Protein design -- 5.1 Introduction -- 5.2 Framework extension -- 5.2.1 Foldit -- 5.2.2 Iteration strategy -- 5.3 Science transfer -- 5.3.1 Visualizations -- 5.3.2 Tools -- 5.3.3 Conditions -- 5.4 Introductory levels -- 5.5 Examples -- 5.5.1 Fibronectin -- 5.5.2 Diels-Alder -- 5.6 Conclusion --
505 8 _a6. Protein structure refinement algorithms -- 6.1 Introduction -- 6.2 Related work -- 6.3 Overview -- 6.3.1 Cookbook -- 6.4 CASP9 analysis -- 6.4.1 Recipe sharing -- 6.4.2 Inheritance relationships -- 6.4.3 Ratings -- 6.5 Script recipe adoption analysis -- 6.6 Algorithm categories -- 6.7 Context dependence -- 6.8 Recipe evolution -- 6.9 Performance comparison -- 6.10 Conclusion --
505 8 _a7. Conclusion -- 7.1 Contributions -- 7.2 Future work -- Bibliography -- Author's biography.
520 3 _aWhen we first set out to create Foldit over six years ago, it wasn't clear that a game-based approach to scientific discovery would work. So we planned from the start for the game to be continually adapting and changing, in order to keep improving based on the lessons we'd learn. It took several years of design, development, and continued iteration from a team of computer scientists and biochemists until the game was at a point where we made our first exciting discovery. The nature of the challenging problems we were facing required this time and refinement to solve. We are now seeing a number of other games that allow players to contribute to scientific research. Much of this growth has been in fields related to biology and biochemistry: EteRNA for designing RNA shapes, EyeWire for mapping neurons, and Phylo for aligning genetic sequences. Each of these games has had exciting scientific results produced by game play. Games are being applied in other areas as well, such as in the Algoraph suite of games for solving graph theory problems. I have been involved in the development of two more science games: Nanocrafter, which aims to push the frontiers of DNA-based synthetic biology, and Flow Jam, which allows players to help formally verify software.
530 _aAlso available in print.
650 0 _aVideo games
_xScientific applications.
650 0 _aFoldit (Game)
650 0 _aProtein folding
_xComputer simulation.
776 0 8 _iPrint version:
_z9781627055048
830 0 _aACM books ;
_v#3.
856 4 8 _3Abstract with links to full text
_uhttp://dx.doi.org/10.1145/2625848
999 _c354
_d354