Evidence map›Paper›PMID 20090166›Full record

ArticleInternational journal of computational biology and drug design2009

Automatic inference of multicellular regulatory networks using informative priors.

Xiaoyun Sun, Pengyu Hong

Abstract read
In one paragraph

Article in International journal of computational biology and drug design, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Xiaoyun SunDepartment of Computer Science, Brandeis University, Waltham, MA 02454, USA. xsun@brandeis.edu
Pengyu Hong

Funding

Intelligent Interfaces for Interactive Analysis of High-Content Cellular ImagesR01EB007042 · NIBIB · BRANDEIS UNIVERSITY · PI HONG, PENGYU · 2007 to 2009
$519k
NIBIB NIH HHS R01 EB007042
6 · The paper itself

Abstract

To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

Indexed as

AlgorithmsAnimalsBayes TheoremCaenorhabditis elegansCell DifferentiationComputational BiologyModels, Biological

Identifiers

PMID20090166
PMCPMC3024031

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.