Inference of TFRNs (2).
Matsuda H.
Animal Study, published in Methods Mol Biol (2014) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Animal Study
- Journal
- Methods Mol Biol (2014)
- Country
- United States
- Reported sample size
- —
- Source database
- PubMed
- PMID
- 24927838
- DOI
- 10.1007/978-1-4939-0805-9_9
Abstract (original English)
This chapter introduces to a study aiming at comprehensively understanding the transcription factor regulatory networks (TFRNs) that govern the process of a cell differentiation. Here we focus on the adipocyte differentiation. For the cell differentiation, we inferred its TFRN using the Bayesian network (BN) method. BNs have been widely used to estimate TFRNs. Many BN methods have been developed to estimate networks from TF expression data. However, BN-based methods require huge computational time to estimate large-scale networks. This chapter introduces to a BN-based deterministic method with reduced computational time. This approach generates all the combinational subnetworks of three TFs, estimates networks of the subnetworks by BN, and unites the networks into a single large network. This method decreases the search space of predicting TFRNs without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. This chapter also presents a massively parallel implementation for the BN-based inference of TFRNs. The system enables us to estimate large-scale (>10,000 transcripts) multiple TFRNs from expression profiles of various tissues and conditions. The comparison among estimated TFRNs of adipose tissues with stimulus induction is conducted. The various regulations to Ucp1 (uncoupled protein 1) in those networks may reflect different responses of the t
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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