Posts Tagged ‘x57l’

IBM Watson Developer Cloud

July 18, 2017

https://www.ibm.com/watson/developercloud/

Post-transcriptional regulation across human tissues

July 16, 2017

Post-transcriptional reg…across…tissues [v genes], by @SlavovLab http://journals.PLoS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005535 Simpson’s paradox! Diff in protein-mRNA corr.

Set Up, Manage and Protect Apple Devices at Work | Jamf Now

July 14, 2017

https://www.jamf.com/lp/set-up-manage-and-protect-apple-devices-at-work

AI for drug discovery – cyan

July 4, 2017

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’

https://medium.com/@mostafab/make-pharma-great-again-with-artificial-intelligence-some-challenges-50e91ea9988d

Clues from the resilient

July 4, 2017

Clues from the resilient
http://www.ScienceMag.org/content/344/6187/970.full Potential 2nd site mutations that neutralize #Mendelian-disease mutations

QT:{{”
“For 127 catastrophic Mendelian diseases (those caused by a single gene such as cystic fibrosis and ataxia-telangiectasia), there are presently 164 genes harboring 685 known recurrent variants that are highly penetrant and causal for deleterious traits, most typically manifesting in individuals before the age of 18 (). …For common diseases, the observed small effect sizes of individual gene variants limit diagnostic potential, and given that most variants identified have an unclear function, how to target the corresponding gene for therapeutic intervention is typically unclear. For rarer Mendelian disorders, although genetics directly implicate a specific gene in a disease, a majority of such cases relate to loss-of-function mutations. Designing small molecules to fix the corresponding broken protein has proven difficult….
The prominent role of second-site mutations and environmental factors that enable resistance to (or buffer against) disease traits has been well established in a multitude of model organisms from yeast to mice (–). Screening for second-site mutations in “resilient” individuals that prevent disease-causing alleles from manifesting their effects could identify targets to which drugs would be designed to disrupt their function, as opposed to targeting the disease-causing gene directly. Genetic studies examining seemingly healthy people have revealed, for example, rare mutations in chemokine (C-C motif) receptor type 5 (the co-receptor for human immunodeficiency virus) that block HIV infection (), and secondary mutations in fetal globin genes that modify the severity of sickle cell disease by buffering primary mutations in β-globin genes ()
“}}

Kyoto Prize – Wikipedia

July 3, 2017

https://en.wikipedia.org/wiki/Kyoto_Prize

FYI – Personalized Medicine: Redefining Cancer Treatment

July 3, 2017

https://www.kaggle.com/c/msk-redefining-cancer-treatment

List of collaboration tools

July 3, 2017

Thought the below was an interesting snippet from a dialogue I had….

QT:{{”

Collaboration tools: Even a small group…will quickly get unwieldy if we coordinate using long CC-chains on email threads.

I think the minimum requirements are central management, email lists, and document sharing.

Options that come to mind are:
– Google G-suite, Drive, and Groups (free for nonprofits, robust features, integrate with existing login…)
– Slack plus Dropbox (more suited for teams who are actively collaborating, but it could be a great framework)
– Yammer (looks great, but I’ve never really used it)
– Office365 (it’s heavyweight and costs money, but it’ll give us all the features we could ever imagine)
– Basecamp (I haven’t used it in years, and it’s more about project management than collaboration)

“}}

Single Cell Analysis paper

June 30, 2017

http://genome-tech.ucsd.edu/public/Lake_Science_2016/
https://twitter.com/mikejg84/status/880240608144531456

16 Neuronal subtypes & [inter-regional] diversity revealed by [#singlecell]-nucleus RNAseq of…the brain
http://science.sciencemag.org/content/352/6293/1586.long

Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain.
Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, Wildberg A, Gao D, Fung HL, Chen S, Vijayaraghavan R, Wong J, Chen A, Sheng X, Kaper F, Shen R, Ronaghi M, Fan JB, Wang W, Chun J, Zhang K.
Science. 2016 Jun 24;352(6293):1586-90. doi: 10.1126/science.aaf1204.

First, design for data sharing : Nature Biotechnology : Nature Research

June 20, 2017

Design for data sharing
http://www.Nature.com/nbt/journal/v34/n4/full/nbt.3516.html Issues in distributing mPower mobile dataset – no DAC, allowing donors to change preferences

QT:{{”
“This March, Sage Bionetworks (Seattle) began sharing curated data collected from >9,000 participants of mPower, a smartphone-enabled health research study for Parkinson’s disease. The mPower study is notable as one of the first observational assessments of human health to rapidly achieve scale as a result of its design and execution purely through a smartphone interface. To support this unique study design, we developed a novel electronic informed consent process that includes participant-determined data-sharing preferences. It is through these preferences that the new data—including self-reported outcomes and quantitative sensor data—are shared broadly for secondary analysis. Our hope is that by sharing these data immediately, prior even to our own complete analysis, we will shorten the time to harnessing any utility that this study’s data may hold to improve the condition of patients who suffer from this disease.

Turbulent times for data sharing

Our release of mPower comes at a turbulent time in data sharing. The power of data for secondary research is top of mind for many these days. Vice President Joe Biden, in heading President Barack Obama’s ambitious cancer ‘moonshot’, describes data sharing as second only to funding to the success of the effort. However, this powerful support for data sharing stands in opposition to the opinions of many within the research establishment. To wit, the august New England Journal of Medicine (NEJM)’s recent editorial suggesting that those who wish to reuse clinical trial data without the direct participation and approval of the original study team are “research parasites”. In the wake of colliding perspectives on data sharing, we must not lose sight of the scientific and societal ends served by such efforts.” “}}