PPAR Research

Biological Network Analysis of PPAR and Related Signaling Pathways 2021

Publishing date
01 Jun 2022
Submission deadline
28 Jan 2022

Lead Editor

1George Mason University, Fairfax, USA

2Elsevier Inc, Rockville, USA

3First Hospital of Shanxi Medical University, Taiyuan, China

This issue is now closed for submissions.

Biological Network Analysis of PPAR and Related Signaling Pathways 2021

This issue is now closed for submissions.


Numerous studies have been conducted to understand peroxisome proliferator-activated receptors (PPAR)-related diseases (over 1,000), genes, and proteins involved in PPAR signaling (over 2,600), compounds targeting this pathway (over 8,000), and the cell processes affected (over 1,000). These studies have built a solid foundation and a valuable knowledge database for functional dissection of PPAR-regulated networks which may be executed either in the wet lab or in silico. Thorough modeling of PPAR signaling and related diseases represents a significant research opportunity as a key contributor towards personalized medicine, due to its potential for precise modulation.

This Special Issue aims to present efforts in the integration of existing databases and knowledge-based algorithms into experimental and computational studies of PPAR signaling and related disorders. Both original research and review articles are welcome, and systematic reviews and meta-analysis studies are also within the scope.

Potential topics include but are not limited to the following:

  • Reviews and research proposing novel avenues for PPAR signaling and related diseases research, with argumentation supported by in silico modeling or aided by knowledge-based algorithms
  • In silico studies of PPAR-regulating compounds, including nutraceuticals, and their effects on healthy and diseased tissues
  • Meta/mega-analysis to build/validate novel/existing biological networks and signaling pathways and reveal PPARs functionalities in diseases
  • Experimental studies in the area of PPAR research, when experimentation is aimed at testing hypotheses derived from predictions made in silico
  • PPAR-related clinical studies rationally designed as a follow-up to analysis of existing knowledge databases
PPAR Research
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Acceptance rate6%
Submission to final decision68 days
Acceptance to publication22 days
Journal Citation Indicator0.720
Impact Factor2.9

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