Posts Tagged ‘networks’

DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data | BMC Systems Biology | Full Text

April 8, 2016

DREM…reconstruction of…regulatory #networks from time-series expression http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-6-104 Classic approach using 3-level IO #HMMs

Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg

February 7, 2016

https://www.cs.cornell.edu/home/kleinber/networks-book/

Understanding multicellular function and disease with human tissue-specific networks : Nature Genetics : Nature Publishing Group

November 28, 2015

Human tissue-specific #networks by @TroyanskayaLab
http://www.nature.com/ng/journal/v47/n6/full/ng.3259.html
Brain-specific ones & NetWAS approach for combining #GWAS genes

access all tissue networks including the brain-specific
networks at giant.princeton.edu

LinkedIn’s Plan for World Domination

October 19, 2015

LinkedIn’s Plan for World Domination http://www.newyorker.com/magazine/2015/10/12/the-network-man No job security, everyone’s an entrepreneur, enlarging prof’l #networks is key

QT:{{"
“Manyika understood that not every chief executive in Silicon Valley could sign the statement, but he was gently trying to pull Hoffman to the left, and he knew how to frame the argument so that it would appeal to him. He went on, “We cannot ignore this problem. Right now, everybody’s punting. We know the share of income that goes to wages is a declining portion, compared with capital expenditures. What does that mean for jobs? Entrepreneurship is part of the answer. Mass-scale entrepreneurship. Before you even get to A.I.”

“You have to be able to let people adapt,” Hoffman said. “You have to have cheap resources to put across the whole system. How do you get inclusion within the tech ecosystem?”

“Very few of the programs have scale,” Manyika said.

“You have to scale to infinite,” Hoffman said.
"}}

Modern Lessons from Ancient Food Webs » American Scientist

August 22, 2015

Modern Lessons from [reconstructing] Ancient Food Webs http://www.americanscientist.org/issues/feature/2015/3/modern-lessons-from-ancient-food-webs/1 Overall #network structure (eg degree dist.) fairly invariant

UPDATED: Bristol-Myers rips up its R&D group, adding, eliminating and moving 800-plus

July 14, 2015

$BMY rips up its R&D group…800-plus [affected]
http://www.fiercebiotech.com/story/bristol-myers-rips-its-rd-group-adding-eliminating-and-moving-hundreds/2015-06-25 Trend of pruning spoke cities & growing #hub ones HT @JorgensenWL

QT:{{”
“Bristol-Myers Squibb’s big reorganization fits into the industry’s new model for R&D. Large, scattered groups are out as big
organizations gravitate toward the big hubs. GlaxoSmithKline ($GSK) and Amgen ($AMGN) have offered two recent examples of that trend, which has benefited hubs like Boston/Cambridge and the Bay Area while inflicting painful cuts in outlying areas. Biopharma companies are also concentrating on core areas, sometime shedding early-stage work–reflected in Merck’s ($MRK) recent downsizing at the newly acquired Cubist and the big asset swap that occurred between GlaxoSmithKline and Novartis ($NVS).”
“}}

Physics in finance: Trading at the speed of light : Nature News & Comment

April 3, 2015

#Physics in finance
http://www.nature.com/news/physics-in-finance-trading-at-the-speed-of-light-1.16872 Real estate opportunites from relativatistic arbitrage: locating exactly midway betw. market hubs

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL

March 27, 2015

RUNX1 is most connected in TLX1 & 3 expr. net. It’s a tumor suppressor disabled by LOF mutations.

Rev. engineering…identifies RUNX1 as tumor suppressor in T-ALL http://www.nature.com/nm/journal/v18/n3/full/nm.2610.html It’s the most connected TF in the expression network

Nat Med. Author manuscript; available in PMC 2012 Sep 1.
Nat Med. 2012 Feb 26; 18(3): 436–440.
Published online 2012 Feb 26. doi: 10.1038/nm.2610

Giusy Della Gatta,1 Teresa Palomero,1,2 Arianne Perez-Garcia,1 Alberto Ambesi-Impiombato,1 Mukesh Bansal,3Zachary W. Carpenter,1 Kim De Keersmaecker,4,5 Xavier Sole,6,7 Luyao Xu,1 Elisabeth Paietta,8,9 Janis Racevskis,8,9Peter H Wiernik,8,9 Jacob M Rowe,10 Jules P Meijerink,11 Andrea Califano,1,3 and Adolfo A. Ferrando1,2,12

Uncovering disease-disease relationships through the incomplete interactome

March 23, 2015

Disease-disease relationships through the incomplete interactome, by @barabasi http://www.sciencemag.org/content/347/6224/1257601.abstract #Network modules for 226 diseases

QT:{{”
Altogether, disease genes associated with 226 of the 299 diseases show a statistically significant tendency to form disease modules based on both Si andP(ds) (fig. S4).
“}}

Linking signaling pathways to transcriptional programs in breast cancer

March 20, 2015

Linking #signaling pathways [phospho-proteins in samples] to transcriptional programs [TFs & targets], via matrices
http://genome.cshlp.org/content/24/11/1869.long

More verbosely:
using matrices to linking TF & targets and phosphorsylated proteins in particular samples to gene expression in specific samples

Linking signaling pathways to transcriptional programs in breast cancer

Hatice U. Osmanbeyoglu1,
Raphael Pelossof1,
Jacqueline F. Bromberg2 and
Christina S. Leslie1

Genome Research