The Research Data Consultation Group (http://researchdata.yale.edu/) has considered aggregating data science training information into a unified calendar.
Also, there’s an instruction calendar at the library
(http://csssi.yale.edu/instruction/workshop-and-instruction-calendar)
The Big Book http://www.Esquire.com/features/robert-caro-0512 Great description of a clinical & cool but productive 4-decade relationship betw. R #Caro & his editor
LBJ #4
QT:{{”
“Gottlieb did the same math and agreed. In an industry that survives mostly by lying to itself, he is an anti-romantic, an
unsentimentalist. When he edits Caro, they sit side by side at a conference table and go through the pile in front of them, page by tattered page, Gottlieb attacking anything that reads too much like writing, too much like nostalgia or indulgence. He and Caro have mellowed with age, but they have fought bitter fights, fights that have caused people to close their office doors hundreds of feet away. “Everything to him is as serious as everything else,” Gottlieb says. “When we came to something like a semicolon, it was war.”
…Gottlieb is the taskmaster. (“I can remember when he told me, ‘Not bad,’ ” Caro says. “Once.”) Gottlieb and Caro, bound for forty years, rarely see each other socially. Theirs is a professional relationship, clear-eyed and clinical.
Yet they are also prisoners of a mutual faith. “Bob is convinced that without me, he cannot function,” Gottlieb says. “I have explained to him for years that it isn’t the truth. It isn’t the truth. But because he believes it to be true, it is true.” And Gottlieb has given over so much of his own life to Caro, has fought so hard over semicolons, because he believes something else to be true. “These books will live forever,” Gottlieb says. “We all know that.””
“}}
The Big Book http://www.Esquire.com/features/robert-caro-0512 Great description of a clinical & cool but productive 4-decade relationship betw. R #Caro & his editor
LBJ #4
QT:{{”
“Gottlieb did the same math and agreed. In an industry that survives mostly by lying to itself, he is an anti-romantic, an
unsentimentalist. When he edits Caro, they sit side by side at a conference table and go through the pile in front of them, page by tattered page, Gottlieb attacking anything that reads too much like writing, too much like nostalgia or indulgence. He and Caro have mellowed with age, but they have fought bitter fights, fights that have caused people to close their office doors hundreds of feet away. “Everything to him is as serious as everything else,” Gottlieb says. “When we came to something like a semicolon, it was war.”
…Gottlieb is the taskmaster. (“I can remember when he told me, ‘Not bad,’ ” Caro says. “Once.”) Gottlieb and Caro, bound for forty years, rarely see each other socially. Theirs is a professional relationship, clear-eyed and clinical.
Yet they are also prisoners of a mutual faith. “Bob is convinced that without me, he cannot function,” Gottlieb says. “I have explained to him for years that it isn’t the truth. It isn’t the truth. But because he believes it to be true, it is true.” And Gottlieb has given over so much of his own life to Caro, has fought so hard over semicolons, because he believes something else to be true. “These books will live forever,” Gottlieb says. “We all know that.””
“}}
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.”
“}}
#PeriodicTable of Technology
http://fios.verizon.com/beacon/periodic-tech-table/ Nicely shows what each chemical element is used for in hi-tech
Periodic Table of Technology
Make Pharma Great Again w. AI, by @mostafabenh
https://Medium.com/@mostafab/make-pharma-great-again-with-artificial-intelligence-some-challenges-50e91ea9988d Optimism-inducing Moore’s law in tech vs. #Eroom’s law for drugs
QT:{{”
Drug discovery is getting increasingly tough and expensive. Despite technological progress, the cost of developing a new drug doubles every nine years. That’s Eroom’s law of Pharma, which mirrors Moore’s law for computer performance.
….
Drugs are getting more expensive
In the tech industry, the situation is different. Optimism prevails. Tech is fueled by Moore’s law, the fact that computer performance is doubling every 18 months.
Moore’s law
This exponential progress keeps prices low. For example, Google gives away the use of its new TPU chip for free, for some scientific projects. Tech companies are more generous due to their feeling of abundance. How can Tech help Pharma, especially at a time of expansion for Artificial Intelligence?
“}}
‘Make Pharma Great Again with Artificial Intelligence: some Challenges’
Genome-wide characterization of..promoters w…enhancer functions http://www.Nature.com/ng/journal/v49/n7/full/ng.3884.html Blurs distinction betw these, suggests flexibility
Genome-wide characterization of mammalian promoters with distal enhancer functions
Lan T M Dao,
Ariel O Galindo-Albarrán,
Jaime A Castro-Mondragon,
Charlotte Andrieu-Soler,
Alejandra Medina-Rivera,
Charbel Souaid,
Guillaume Charbonnier,
Aurélien Griffon,
Laurent Vanhille,
Tharshana Stephen,
Jaafar Alomairi,
David Martin,
Magali Torres,
Nicolas Fernandez,
Eric Soler,
Jacques van Helden,
Denis Puthier
& Salvatore Spicuglia
Promoting transcription over long distances
Rui R Catarino,
Christoph Neumayr
& Alexander Stark
Nature Genetics 49, 972–973 (2017) doi:10.1038/ng.3904
28 June 2017
http://www.nature.com/ng/journal/v49/n7/full/ng.3884.html
http://www.nature.com/ng/journal/v49/n7/full/ng.3904.html
QT:{{”
“Should we be surprised that promoters can function as enhancers—or better—that enhancers and promoter regions can overlap? Probably not: the habit of annotating different genomic regions with distinct labels ignores the fact that DNA sequences typically encode different genetic functions in a rather flexible manner. Enhancers and promoters are determined by the presence of short degenerate motifs, and even protein-coding regions display flexibility due to the degeneracy of the genetic code. Therefore, a single DNA sequence can encode different types of functions, including enhancer function of protein-coding regions or—as shown now—enhancer function of
promoters.”
“}}
UCB & @BitmarkInc partner to bring data donation to public health
studies https://Medium.com/@bitmark/uc-berkeley-and-bitmark-partner-to-bring-data-donation-to-public-health-studies-3e9a17891432 Using #blockchain to control one’s data
Partitioning heritability of regulatory…variants across 11 common diseases http://www.Cell.com/ajhg/abstract/S0002-9297(14)00426-1 Almost 80% #noncoding v 10% coding
The paper below claims to find most of the heritability of 11 common diseases in regulatory regions (79% of heritability found in regulatory regions, <10% in protein coding regions).
Partitioning heritability of regulatory and cell-type-specific variants across 11 common
diseases.
Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ, Xu H, Zang C, Ripke S, Bulik-Sullivan B, Stahl E; Schizophrenia Working Group of the Psychiatric Genomics Consortium; SWE-SCZ Consortium, Kähler AK, Hultman CM, Purcell SM, McCarroll SA, Daly M, Pasaniuc B, Sullivan PF, Neale BM, Wray NR, Raychaudhuri S, Price AL; Schizophrenia Working Group of the Psychiatric Genomics Consortium; SWE-SCZ Consortium.
Am J Hum Genet. 2014 Nov 6;95(5):535-52. doi:
10.1016/j.ajhg.2014.10.004. Epub 2014 Nov 6.