linkstream2 microblog

http://arxiv.org/abs/1308.5257

@mims2m:
Weekend read: Why you need middle management, you feckless layabout

Hierarchy is good: Nice argument that hierarchies are essential in any #social setting
http://www.linkedin.com/today/post/article/20140112221140-15893932-hierarchy-is-good-hierarchy-is-essential-and-less-isn-t-always-better HT @Chris_Gammell @mims (1/2)

.@Chris_Gammell @mims Perhaps related to middle managers being fundamental for info flow in molecular #networks
http://www.ncbi.nlm.nih.gov/pubmed/20351254 (2/2)

http://www.linkedin.com/today/post/article/20140112221140-15893932-hierarchy-is-good-hierarchy-is-essential-and-less-isn-t-always-better

QT:{{”
BioLayout Express3D has been specifically designed for visualization, clustering, exploration and analysis of very large network graphs in two- and three-dimensional space derived primarily, but not
exclusively, from biological data.
“}}
www.biolayout.org

Romantic Insights From Maps of Facebook #Networks: Many mutual friends don’t necessarily indicate a good match
http://bits.blogs.nytimes.com/2013/10/28/spotting-romantic-relationships-on-facebook

.@randal_olson @thatdnaguy Lots of interesting #network datasets available from http://snap.stanford.edu/data . Thanks for pointing out this site!
http://snap.stanford.edu/data

Content Disputes in #Wikipedia Reflect Geopolitical Instability: bio #network ideas applied to social context
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0020902

Content Disputes in Wikipedia Reflect Geopolitical Instability http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0020902

(guilt-by-association)

The opposite of clique completion: #Network deconvolution.. to distinguish direct dependencies http://go.nature.com/dVzNwC via @taziovanni

Network deconvolution as a general method to distinguish direct dependencies in networks

Soheil Feizi, Daniel Marbach, Muriel Médard & Manolis Kellis

http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2635.html

My thoughts:

Indirect relationships in a network can confound the inference of true direct relationships in a network. T, so this paper sought to develop a quantitative framework, termed network deconvolution (ND), to infer direct relationships and remove false positives in a network by quantifying and then removing indirect transitive relationship effects. The mathematical framework assumes that (1) an indirect relationship (edge) can be approximated as the product of its component direct edges and that (2) the observed edge weights are the sum of the direct and indirect edge weights – a linear dependency. The main application seems to be in mutual information (MI) and
correlation-based (COR) networks. They applied ND to various scenarios such as local network connectivity prediction (FFL
prediction), gene regulatory network prediction (in E. coli), prediction of interacting amino acids in protein structures (MI network) and coauthorship relationship network and found that (1) it can be used with various networks beyond just MI and COR (2) it can be used alone or more powerfully in combination with existing
methods/algorithms to improve predictions. In a sense it is the opposite of clique and module completion approaches (such as k-core).

graph analysis of clustering of TED talks, which are central to subclusters, which link, &c

http://www.ted.com/talks/eric_berlow_and_sean_gourley_mapping_ideas_worth_spreading.html

World’s Most Influential Thinkers Revealed by #network #analysis: Being book-published economist is key
http://www.technologyreview.com/view/518026/network-analysis-reveals-worlds-most-influential-thinkers via @atripper

http://www.technologyreview.com/view/518026/network-analysis-reveals-worlds-most-influential-thinkers

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12337.html#

QT:”
We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. …Using ChIP-Seq combined with expression
data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors….The regulatory network reveals transcription factors
underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub. “