Posts Tagged ‘i0aibos’

AI Pharma Innovation Summit 2017 – Final Event Information

July 27, 2017

http://ai-pharma.com/event-guide/

https://linkstream2.gerstein.info/tag/i0aibos/

A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a s tudy conducted with personality disorder samples | BMC Medical Research Methodology | Full Text

July 27, 2017

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-61

Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review … – Paul Meehl – Google Books

July 27, 2017

Comfort cites classic work by P Meehl: Clinical v Statistical
Prediction https://books.google.com/books/about/Clinical_Versus_Statistical_Prediction.html #AIPharma @AIPharma_Summit

AI Genomics Hackathon — Silicon Valley Artificial Intelligence

July 27, 2017

https://sv.ai/hackathon/

Shaun Comfort’s profile

July 27, 2017

https://www.slideshare.net/mobile/ShaunComfort

UCSF Cancer Researcher Leads Team to Win First Ever AI Genomics Hackathon | UC San Francisco

July 27, 2017

https://www.ucsf.edu/news/2017/07/407661/ucsf-cancer-researcher-leads-team-win-first-ever-ai-genomics-hackathon

Startup Founder’s Quest for Cure Leads to Genomics Hackathon at Google | Xconomy

July 27, 2017

http://www.xconomy.com/san-francisco/2017/06/23/startup-founders-quest-for-cure-leads-to-genomics-hackathon-at-google/

Quick comment on AI for pharma?

July 18, 2017

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.”
“}}