Posts Tagged ‘networks’

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

Tuesday, July 14th, 2015

$BMY rips up its R&D group…800-plus [affected] Trend of pruning spoke cities & growing #hub ones HT @JorgensenWL

“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

Friday, April 3rd, 2015

#Physics in finance 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

Friday, March 27th, 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 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

Monday, March 23rd, 2015

Disease-disease relationships through the incomplete interactome, by @barabasi #Network modules for 226 diseases

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

Friday, March 20th, 2015

Linking #signaling pathways [phospho-proteins in samples] to transcriptional programs [TFs & targets], via matrices

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

Reconciling differential gene expression data with molecular interaction networks

Wednesday, January 28th, 2015

Reconciling differential gene expression
w/…#networks Propagating this across interactions finds perturbed pathways

This paper basically propagates scores of disease-related highly differentially expressed genes (-log10 p) over human protein interaction network, calculates new scores using four major algorithms (Vanilla, PageRank, GeneMANIA, Heat Kernel), re-ranks genes based on the new scores and then finds enriched pathways among top-ranking genes. Compared with traditional ways by ranking highly differentially expressed genes based on p-values without any network information, the approach not only recovered canonical pathways but also discovered novel ones such as an insulin-mediated glucose transport pathway in Huntington’s disease. The authors also explored differences among four algorithms and identified the top-ranking genes specifically found by particular algorithms. In short, the paper provides a valuable framework for integrating networks and gene expression data. Their analysis for comparing four major algorithms is also helpful.

Distributed Information Processing in Biological and Computational Systems

Monday, January 26th, 2015

Distributed Info. Processing in Biological & Computational #Systems Contrasts in strategies to handle node failures

While both computational and biological systems need to address these similar types of failures, the methods they use to do so differs. In distributed computing, failures have primarily been handled by majority voting methods,37 by using dedicated failure detectors, or via cryptography. In contrast, most biological systems rely on various network topological features to handle failures. Consider for example the use of failure detectors. In distributed computing, these are either implemented in hardware or in dedicated additional software. In contrast, biology implements implicit failure detector mechanisms by relying on backup nodes or alternative pathways. Several proteins have paralogs, that is, structurally similar proteins that in most cases originated from the same ancestral protein (roughly 40% of yeast and human proteins have at least one paralog). In several cases, when one protein fails or is altered, its paralog can automatically take its place24 or protect the cell against the mutation.26 Thus, by preserving backup functionality in the protein interaction.

While we discussed some reoccurring algorithmic strategies used within both types of systems (for example, stochasticity and feedback), there is much more to learn in this regard. From the distributed computing side, new models are needed to address the dynamic aspects of communication (for example, nodes joining and leaving the network, and edges added and being subtracted), which are also relevant in mobile computing scenarios. Further, while the biological systems we discussed all operate without a single centralized controller, there is in fact a continuum in the term “distributed.” For example, hierarchical distributed models, where higher layers “control” lower layers with possible feedback, represent a more structured type of control system than traditional distributed systems without such a hierarchy. Gene regulatory networks and neuronal networks (layered columns) both share such a hierarchical structure, and this structure has been well-conserved across many different species, suggesting their importance to computation. Such models, however, have received less attention in the distributed computing literature.


BMC Bioinformatics | Abstract | Sensitive detection of pathway perturbations in cancers

Tuesday, December 23rd, 2014

Sensitive detection of pathway perturbations in #cancers Differential expression of #pathways (in toto or a sub-part)

** Sensitive detection of pathway perturbations in cancers. Rivera et al. BMC Bioinfo (2014)

In this paper, the authors introduce a new computational method that identifies subsets of pathways that exhibit differential gene expression between cancer and normal tissue. Some previous methods only considered differential expression in sets of genes without considering the structure of interactions between the genes or their protein products. Other previous methods looked at pathway
perturbations, but required all members of a pathway to exhibit differential gene expression in order for the pathway to appear significant. The authors demonstrate the general superiority of their method to these previous methods, as well as the robustness of their method to missing data. The authors also consider future enhancements, such as taking into account the direction of differential expression, using more information on the nature of each gene interaction involved, and using universal protein interaction networks to incorporate data beyond what is found in curated pathway databases.

A load driver device for engineering modularity in biological networks : Nature Biotechnology : Nature Publishing Group

Tuesday, December 9th, 2014

A load driver device for engineering modularity in…#networks Allows joining components w/o downstream retroactivity

Twitter “Exhaust” Reveals Patterns of Unemployment | MIT Technology Review

Monday, December 1st, 2014

Social media fingerprints of unemployment, from detecting network components in tweet mining +

Lots of press for an arxiv paper, viz:
Twitter “Exhaust” Reveals Patterns of Unemployment | MIT Technology Review


So the team analysed the rate at which messages were exchanged between regions using a standard community detection algorithm. This revealed 340 independent areas of economic activity, which largely coincide with other measures of geographic and economic distribution. “This result shows that the mobility detected from geolocated tweets and the communities obtained are a good description of economical areas,” they say.

Finally, they looked at the unemployment figures in each of these regions and then mined their database for correlations with twitter activity.