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.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. “
Great primer on network theory MT @russskinner PRISM… analysis: Paul Revere identified by his connections to others http://bit.ly/11DK8R9
http://www.slate.com/articles/health_and_science/science/2013/06/prism_metadata_analysis_paul_revere_identified_by_his_connections_to_other.single.html
a detailed comparison of
MetaCyc versus KEGG:
MetaCyc contains more than 10 times as many metabolic pathways as KEGG “modules” (the comparable type of pathway in KEGG).
http://www.biomedcentral.com/1471-2105/14/112/abstract
http://www.ncbi.nlm.nih.gov/pubmed/17369648
Comput Syst Bioinformatics Conf. 2006:299-310.
Evolution versus “intelligent design”: comparing the topology of protein-protein interaction networks to the Internet.
Yang Q, Siganos G, Faloutsos M, Lonardi S.
linkstream2 microblog 