ReviewWiley interdisciplinary reviews. Systems biology and medicine
Toward a complete in silico, multi-layered embryonic stem cell regulatory network.
Review in Wiley interdisciplinary reviews. Systems biology and medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- A conceptual and computational framework for modelling and understanding the non-equilibrium gene regulatory networks of mouse embryonic stem cells.PLoS computational biology · 2017Article
- Computational inference of a genomic pluripotency signature in human and mouse stem cells.Biology direct · 2016Article
- BTR: training asynchronous Boolean models using single-cell expression data.BMC bioinformatics · 2016Article
- A blueprint of cell identity.Nature biotechnology · 2014Article
- ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells.Database : the journal of biological databases and curation · 2013Article
- Sets2Networks: network inference from repeated observations of sets.BMC systems biology · 2012Article
- Translational bioinformatics: linking the molecular world to the clinical world.Clinical pharmacology and therapeutics · 2012Review
- SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells.BMC systems biology · 2010Article
- The PluriNetWork: an electronic representation of the network underlying pluripotency in mouse, and its applications.PloS one · 2010Article
- ExprEssence--revealing the essence of differential experimental data in the context of an interaction/regulation net-work.BMC systems biology · 2010Article
- 2010 translational bioinformatics year in review.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.
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What OpenQuestion holds
Registered trials
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.