Posts Tagged ‘network’

PLOS Genetics: Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network

Sunday, December 28th, 2014

Correlated Genome Associations to Quantitative Trait #Network (QTN)
Uses fused #lasso for estimation of relationships

Kim & Xing (’09) provide a new method for calculating how genetic
markers associate with phenotypes by incorporating phenotype
connectivity features into the correlation structure between markers
and phenotypes. Their model attempts to quantify pleiotropic
relationships between different phenotypes and assumes a common
genotypic origin for the existence of clusters of correlated
phenotypes, which their algorithm uses to reduce the number of
significant genetic markers. In particular, Kim and Xing present a
method for performing quantitative trait analysis that implements two
novel approaches to inferring the contribution of a
[marker/allele/SNP/gene/locus] to a quantitative trait. The first is
organization of traits into a quantitative trait network (QTN). The
second is the utilization of fused lasso, a variation of multivariate
regression that seeks to minimize the number of non-zero coefficients
and least squared error. These two approaches are combined in an
attempt to minimize noise (in the form of small coefficients for SNP’s
that don’t really make a contribution) and focus on truly relevant
SNP’s while dealing with the correlated nature of quantitative
traits. Based on two datasets – simulated HapMap data and
data from the Severe Asthma Research Program – the authors show marked
improvement in accuracy and reduction of false positives over simpler
multivariate regression methods.

VIRGO: computational prediction of gene functions

Thursday, December 25th, 2014

VIRGO: computational prediction of gene function Webserver propagates GO terms over PPI & gene-expression #networks

This work was said to be the first web server for gene function
annotation (not the first algorithm).
The idea is to predict gene functions from known molecular interaction
networks (such as PPI), which includes both annotated and unannotated
genes. The potential function of an unannotated gene is predicted
using a propagation diagram, which takes into account the neighbors’
functions. The weight of edge in the network is determined by user uploaded expression data. Weight = |Pearson correlation| of expression
profiles of the gene pair. Weight reflects the confidence of the edge.

Dissecting Disease Inheritance Modes in a Three-Dimensional Protein Network Challenges the “Guilt-by-Asso ciation” Principle

Thursday, August 7th, 2014

Inheritance Modes in… #Network Challenges… Guilt-by-Association #Diseases of recessive interface SNVs predictable

surprisingly, no positional effects on LOF mutations … significant proportion of truncation alleles give rise to functional products

“guilt by assoc works”

signif. number dom mut give rise to func products

PLOS Biology: The Ecology of Collective Behavior

Friday, July 11th, 2014

#Ecology of Collective Behavior #Network structure reflects resource patchiness, operating costs & threat of rupture

Nature Genetics calls for data analysis papers

Friday, May 9th, 2014

Call for… analysis papers: nice description of hypothesis generation v. validation & 3 types of #network robustness

Call for data analysis papers
Nature Genetics 46, 213 (2014) doi:10.1038/ng.2914Published online 26 February 2014
Deadline expired but maybe still relevant…

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Sunday, April 27th, 2014

Protein… #network of alternatively spliced isoforms from brain…: Half of the interactions from #splicing variants

Access : Surfing the p53 network : Nature

Saturday, April 12th, 2014

Original article emphasizing the importance of networks to cancer

A paper that was done by Vogelstein, Lane and Levine in Nature (2000, November 16) that talks about how cancer is associated with a network and these network of genes associated with p53. This is in response to the idea that p53 is such a crucial molecule in cancer as a tumor suppressor and it marks well known cancer biologists discussing this from a network perspective.

facebook global network 2011

Saturday, March 3rd, 2012