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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

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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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Thomson Reuters HIGHLY CITED RESEARCHERS (HCR) List

ON:
2016_HCR_as_of_November_16_2016.xlsx
2015_HCR_as_of_December_1_2015.xlsx
2014_HCR_as_of_September_8_2015.xlsx
2014_HCR_List_as_of_December_31_2014.xlsx

2016 HCR List as of November 16 2016
2015 HCR List as of December 1 2015
2014 HCR List as of December 31 2014
2014 HCR as of September 8 2015

NOT-ON:
2001_HCR_List_as_of_December_31_2001.xlsx
2001_HCR_as_of_September_8_2015.xlsx

2001 HCR List as of December 31 2001
2001 HCR as of September 8 2015

http://hcr.stateofinnovation.thomsonreuters.com/page/archives http://hcr.stateofinnovation.thomsonreuters.com/

MG mentioned on
http://physics.yale.edu/news/steven-girvin-robert-schoelkopf-and-nikhil-padmanabhan-among-most-influential-scientific-minds Thomson Reuter’s
THE WORLD’S MOST INFLUENTIAL SCIENTIFIC MINDS 2015
in 2 categories: Biochem & Genetics

http://news.yale.edu/2015/07/20/making-miniature-brains-skin-cells-better-understand-autism

http://news.yale.edu/2015/09/30/research-news-catalogue-human-genetic-variation-revealed

http://news.yale.edu/yale-researchers-participate-study-genetics-multi-substance-use-among-veterans

http://www.the-scientist.com/?articles.view/articleNo/42950/title/Microbiome-Fingerprints/

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As microbiome signatures mature, law enforcement or intelligence agents could theoretically track people by looking for traces of them left in the microbes they shed. Mark Gerstein, who studies biomedical informatics at Yale University and was not involved in the new study, suggested, for instance, that one could imagine tracking a terrorist’s movements through caves using their microbiome signature.

Huttenhower and his colleagues were identifying individuals out of pools of just hundreds of project participants, however. It is currently unclear how well the algorithm will perform when applied to the general population, though the researchers estimate that their code could likely pick someone out from a group of 500 to 1,000. “I would expect that number to get bigger in the future as we get more data and better data and better coding strategies,” Huttenhower said.

But the work raises privacy concerns similar to those faced by scientists gather human genomic data. Microbiome researchers are already wary of the human genomic DNA that gets caught up in microbiome sequences, but it increasingly appears that the microbiome sequences themselves are quite personal.

In the genomics field, researchers have increasingly limited access to databases containing human genomic sequencing data. Researchers must apply to use these data. “People might increasingly want to put the microbiome data under the same type of protection that they put normal genomic variants under,” said Gerstein. “Your microbiome is associated with various disease risks and proclivities for X and Y. I don’t think it’s a completely neutral identification. It potentially says things about you.”

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http://yaledailynews.com/blog/2015/03/26/seas-receives-20-million-donation/

https://www.quantamagazine.org/20141218-machine-intelligence-cracks-genetic-controls/

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The splicing code is just one part of the noncoding genome, the area that does not produce proteins. But it’s a very important one. Approximately 90 percent of genes undergo alternative splicing, and scientists estimate that variations in the splicing code make up anywhere between 10 and 50 percent of all disease-linked mutations. “When you have mutations in the regulatory code, things can go very wrong,” Frey said.

“People have historically focused on mutations in the protein-coding regions, to some degree because they have a much better handle on what these mutations do,” said Mark Gerstein, a bioinformatician at Yale University, who was not involved in the study. “As we gain a better understanding of [the DNA sequences] outside of the protein-coding regions, we’ll get a better sense of how important they are in terms of disease.”

Scientists have made some headway into understanding how the cell chooses a particular protein configuration, but much of the code that governs this process has remained an enigma. Frey’s team was able to decipher some of these regulatory regions in a paper published in 2010, identifying a rough code within the mouse genome that regulates splicing. Over the past four years, the quality of genetics data — particularly human data — has improved dramatically, and
machine-learning techniques have become much more sophisticated, enabling Frey and his collaborators to predict how splicing is affected by specific mutations at many sites across the human genome. “Genome-wide data sets are finally able to enable predictions like this,” said Manolis Kellis, a computational biologist at MIT who was not involved in the study.

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http://www.designntrend.com/articles/32141/20141223/ai-teams-up-genomics-find-disease-causing-mutations.htm