The Downside of #Baseball’s Data Revolution – Long Games, Less Action
https://www.WSJ.com/articles/the-downside-of-baseballs-data-revolutionlong-games-less-action-1507043924 It’s now a game for stat analysis, not thrills
Please find the article at link:
https://www.pharma-iq.com/informatics/articles/is-big-pharma-really-on-cusp-of-ai-shake-out-0
Is big pharma really on cusp of AI shake-out?
By: Pharma IQ
Posted: 07/14/2017
QT:{{”
The promises of “disruptive technologies” have failed to live up to expectations in the past. For example, the development of ‘high throughput screening’ – a process that employs robotics to conduct millions of chemical, genetic and pharmacological tests in rapid time – in the 1990s failed to significantly reduce R&D inefficiencies and offered sporadic success rates.
“The major cost in drug R&D is last-phase clinical trials,” said Dr Mark Gerstein, professor of biomedical informatics at Yale University. “It is not clear whether AI can be as useful for these as it has been in target selection for the initial phases.”
“One of the first principles of data mining is that history is a good predictor of the future. AI has a track record of not living up to its expectations and therefore caution about how great its impact will be in the healthcare industry is now warranted.”
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They’re Watching You at Work http://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681 Will HR analytics be a corporate big brother or personal coach? #Datamining & #Privacy
– apply to metabric consortium
– 17K clin feat. + ~50K gene exp. + ~30K CNVs ==to-predict==> 10yr survival – uses CI instead of AUC for real valued predictions
– combine collaboration & competition to beat the baseline (cox regression on only clinical features)
– mol. feat. on their own don’t work well due to the curse of dimensionality – features more important than the learning method
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003047
Pandey mentions: Cancer Survival Analysis through
Competition-Based…Modeling, using Human #Ensembles
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003047 #kdd2014
Pandey mentions: Comparative Analysis of #Ensemble Classifiers [eg mean agg. or stacking]…in Genomics
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6729565&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6729565 #kdd2014
performance-diversity tradeoff: should one incl. higher performance, lower diversity ones…. but still adding diversity is good
related to https://github.com/shwhalen/datasink
Rich C, Alexandru N-M, Geoff C, Alex K (2004) Ensemble selection from libraries of
models. Proceedings of the twenty-first international conference on Machine learning. Banff, Alberta, Canada: ACM.
http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf
Thomas GD (2000) Ensemble Methods in Machine Learning. Proceedings of the First International Workshop on Multiple Classifier Systems: Springer-Verlag.
http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/papers/Dietterich00.pdf http://dl.acm.org/citation.cfm?id=743935
.@deniseOme Good ref is TG Dietterich #Ensemble Methods in
#MachineLearning MCS ’00
http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/papers/Dietterich00.pdf Not rel. to @ensembl #ismb #afp14
ref 17 & 18
Facebook Could Decide an Election—Without You Ever Finding Out. @zittrain advocates regulating digital gerrymandering
http://www.newrepublic.com/article/117878/information-fiduciary-solution-facebook-digital-gerrymandering