Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,330 | 0,186 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».