Evidence map›Paper›PMID 23210478›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2012

Optimization-based inference for temporally evolving networks with applications in biology.

Young Hwan Chang, Joe Gray, Claire Tomlin

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Young Hwan ChangDepartment of Mechanical Engineering, University of California, Berkeley, CA 94720-1770, USA.
Joe Gray
Claire Tomlin

Funding

Systems-Based Predictions of Responses to Cancer TherapyU54CA112970 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI GRAY, JOE W. · 2004 to 2014
$27.1M
NCI NIH HHS U54 CA 112970
6 · The paper itself

Abstract

The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.

Indexed as

AlgorithmsGene Regulatory NetworksModels, BiologicalSignal TransductionBreast NeoplasmsComputational BiologyComputer SimulationErb-b2 Receptor Tyrosine KinasesFemaleHumansNonlinear DynamicsErb-b2 Receptor Tyrosine Kinases

Identifiers

PMID23210478
PMCPMC3513986

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.