Addressing the minimum fleet problem in on-demand urban mobility by @mmvazifeh, @stevenstrogatz et al.
https://www.Nature.com/articles/s41586-018-0095-1 Solved by putting taxis & their riders into a large #network
#Network propagation [by Markov walks, heat flow, diffusion, &c]: a universal amplifier of genetic associations
http://www.Nature.com/nrg/journal/v18/n9/abs/nrg.2017.38.html
Lenore Cowen,
Trey Ideker,
Benjamin J. Raphael
& Roded Sharan
http://www.nature.com/nrg/journal/v18/n9/abs/nrg.2017.38.html?foxtrotcallback=true
Naldi, A. et al. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells. PLOS Computational Biology 13, e1005432 (2017).
Reconstruction & signal propagation analysis of the Syk signaling #network http://journals.PLoS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005432 Inferring potential targets of the kinase
WGS & Social-#Network Analysis of a TB Outbreak http://www.NEJM.org/doi/full/10.1056/NEJMoa1003176 Nice tech combo but not sure transmission & phylogeny are consistent
Mind the gaps: The holes in your brain…make you smart
https://www.NewScientist.com/article/mg23331180-300-mind-the-gaps-the-holes-in-your-brain-that-make-you-smart/ Contrasts connectivity from graphs vs large-scale topology
#Network Analysis [highlighting #modularity change]: Animation shows how divided Congress has become over…60 yrs https://www.YouTube.com/watch?v=tEczkhfLwqM
Scored…PPI #network to catalyze genomic interpretation http://www.Nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4083.html >500k links from lit. mining; up weights small-scale expt
Spatiotemporal…Protein Network Implicates Cortical…Fetal Brain Development & KCTD13…RhoA Pathway in…Diseases
http://www.sciencedirect.com/science/article/pii/S0896627315000367
dyanamic PPI w brainspan data
Spatiotemporal 16p11.2 Protein Network Implicates Cortical Late Mid-Fetal Brain Development and KCTD13-Cul3-RhoA Pathway in
Psychiatric Diseases
Guan Ning Lin1, 5,
Roser Corominas1, 5,
Irma Lemmens2,
Xinping Yang3,
Jan Tavernier2,
David E. Hill3,
Marc Vidal3,
Jonathan Sebat1, 4,
Lilia M. Iakoucheva1,
Kinome-wide Decoding of #Network-Attacking Mutations Rewiring Cancer http://www.cell.com/cell/abstract/S0092-8674(15)01108-3 Mapping NAMs onto well-known signaling pathways
Computer Vision
&…Hallucinationshttp://www.americanscientist.org/issues/id.16420,y.2015,no.5,content.true,page.1,css.print/issue.aspx Instead of training a neural network, train an image to fit it. Dreams emerge
QT:{{”
“The algorithm behind the deep dream images was devised by Alexander Mordvintsev, a Google software engineer in Zurich. In the blog posts he was joined by two coauthors: Mike Tyka, a biochemist, artist, and Google software engineer in Seattle; and Christopher Olah of Toronto, a software engineering intern at Google.
Here’s a recipe for deep dreaming. Start by choosing a source image and a target layer within the neural network. Present the image to the network’s input layer, and allow the recognition process to proceed normally until it reaches the target layer. Then, starting at the target layer, apply the backpropagation algorithm that corrects errors during the training process. However, instead of adjusting connection weights to improve the accuracy of the network’s response, adjust the source image to increase the amplitude of the response in the target layer. This forward-backward cycle is then repeated a number of times, and at intervals the image is resampled to increase the number of pixels.”
“}}