https://www.nature.com/articles/ng1197-260
https://www.nature.com/articles/ng1197-260.pdf?proof=t2019-8-23
Posts Tagged ‘to’
Genetics goes to Hollywood | Nature Genetics
November 25, 2022What’s next for AlphaFold and the AI protein-folding revolution
November 25, 2022Display the file extensions in Finder | defaults-write.com
October 30, 2022https://www.defaults-write.com/display-the-file-extensions-in-finder/#more-1354 {{”
defaults write NSGlobalDomain AppleShowAllExtensions -bool true killall Finder
“}}
defaults-write.com might have seq. of commands to save all of one’s settings
How to preserve secrets in a quantum age | The Economist
August 20, 2022Secondary_appt Great article on our faculty growth
August 3, 2022How ‘Trustless’ Is Bitcoin, Really? – The New York Times
June 18, 2022QT:{{”
Mark Gerstein, a professor of bioinformatics at Yale University, found in the research implications for data privacy. He recently stored a genome on a private blockchain, which allowed for a secure and tamperproof record. But he noted that in a public setting, as with Bitcoin’s blockchain, a data set’s size and subtle patterns made it susceptible to breaches, even as the data remained immutable. (Ms. Blackburn wasn’t tampering with the Bitcoin blockchain’s records.)
“That’s the amazing thing about big data,” Dr. Gerstein said. “If you have a big enough data set, it starts to leak information in unexpected ways.” Even more so when data from different sources are connected, he said: “When you combine one data set with another to make a bigger data set, nonobvious linkages can arise.”
“}}
https://www.nytimes.com/2022/06/06/science/bitcoin-nakamoto-blackburn-crypto.html
2022-06-08-09.34.24.NY-Times-story-on-bitcoin.x78qt.jpg
2022-06-08-09.36.23.NY-Times-story-on-bitcoin.x78qt.jpg
Mutation bias reflects natural selection in Arabidopsis thaliana | Nature
April 14, 2022https://www.nature.com/articles/s41586-021-04269-6
https://twitter.com/MarkGerstein/status/1510065060806283269
Interesting paper, relating de novo mutation rate to epigenetic features. Wonder exactly how this connects to the fact that the background mutation rate in cancer genomes depends strongly on epigenetics.
Important genomic regions mutate less often than do other regions https://www.nature.com/articles/d41586-022-00017-6
Biologically informed deep neural network for prostate cancer discovery | Nature
April 12, 2022https://www.nature.com/articles/s41586-021-03922-4
https://twitter.com/SEHanlon/status/1513548800949989377
At #AACR22, @VanAllenLab gives a nice overview of his paper using an interpretable NN model to get insights into determining cancer severity (https://nature.com/articles/s41586-021-03922-4)
Biologically informed deep neural network for prostate cancer discovery
Liked the way he hard-coded specific genes & pathways into the model & looked in detail where the model misclassified specific patients #AACR22 #AACR2022
Also, thought the hard-coding of genes into the model was similar to that in another interpretable AI approach (for brain disease, https://science.org/doi/10.1126/science.aat8464) Could have used the “rank projection trees” from this to highlight important genes #AACR22 #AACR2022
Kuramoto Model of Synchronization
March 23, 2022the Kuramoto model of synchronization that could explain tissue level behavior from individual cells. Here is a nice YouTube video that explain this type of physical behavior with the Cornell professor in dynamic systems and chaos Steven Strogatz:
linkstream2 microblog 