Bibliographic record
Abstract
Author : Guerrilla Tea Publisher: Cancer Research, UK Price: Free to Play Year of Publication: 2013 https://play.google.com/store/apps/details?id=com.guerillatea.elementalphaandhl=en. https://itunes.apple.com/gb/app/play-to-cure-genes-in-space/id784643890?mt=8. Gamification for data mining as on aid to cancer research: Cancer Research UK recently got together with Amazon Web Services, Facebook and Google, to launch an initiative that was a 3-day game jam in London to see if there was scope to help speed up the gene research process through play. Along with 40 academics, coders, graphic designers and other technically-minded individuals, made an attempt to turn the reading of raw gene data into a game concept. A game jam usually restricts the generation of a game concept to a few days, picks the team with the most interesting or feasible idea, and then has financial support to make it into a final product. In this case, the game "play to cure: Genes in space" was developed by Scottish indie studio Guerrilla Tea. Scientists found that if there are many people playing the game compared with a single individual, and a single level in the game, the analysis and research data acquired would be achieved more rapidly. The game involves a simple concept of flying a spaceship through a route defined by the player, and then has to avoid asteroids from damaging the ship, while completing the path assigned and collecting a fictional element known as element alpha. Games like this employ Gamification, which in short is the process of making a mundane task more involving and useful as far as productivity is concerned. The cancer research UK website states that, "by playing genes in space you′ll be analyzing significant amounts of genetic data that would have taken scientists hours to do. This data can then be used to develop new life-saving treatments." Another insight given by the website is that, "in a nutshell, by finding the best route to pick up the most Element Alpha, you′re actually plotting a course through genuine "DNA microarray" data." http://www.cancerresearchuk.org/support-us/play-to-cure-genes-in-space. Speaking to the Telegraph, Dr. Oscar Reuda one of the scientific team behind the project, says researchers "gathered 2,000 breast cancer tumor samples from patients in the UK and Canada." From these breast cancer tumor samples, 46,000 data sets have been created, and they all now need to be assessed to look for these DNA faults. "That's a lot of data," explains Dr. Rueda. "And our researchers simply don′t have the time to go through it all. Hence, we use computers. But they are making mistakes up to 10% of the time." http://www.telegraph.co.uk/sponsored/health/scientific-breakthroughs/10616684/cancer-research-play-cure.html. On a personal note, the game is presented well, holds a good structure and has enough features to make it fun and engaging to a broad audience. In the end, the more people who play the game regardless of how much time spent on it will be aiding the entire spectrum of Cancer Treatment to a certain level. The game is available for free to download and play on both the iOS and Android devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.330 | 0.186 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".