ArticleFrontiers in physiology2017
Co-expression Network Approach Reveals Functional Similarities among Diseases Affecting Human Skeletal Muscle.
Article in Frontiers in physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- eQTLs as causal instruments for the reconstruction of hormone linked gene networks.Frontiers in endocrinology · 2022Review
- Genomics and transcriptomics landscapes associated to changes in insulin sensitivity in response to endurance exercise training.Scientific reports · 2021Article
- Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction.eLife · 2021Article
- Skeletal muscle: A review of molecular structure and function, in health and disease.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
- Personalized Medicine and Molecular Interaction Networks in Amyotrophic Lateral Sclerosis (ALS): Current Knowledge.Journal of personalized medicine · 2018Review
- Prediction of Molecular Mechanisms for LianXia NingXin Formula: A Network Pharmacology Study.Frontiers in physiology · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Diseases affecting skeletal muscle exhibit considerable heterogeneity in intensity, etiology, phenotypic manifestation and gene expression. Systems biology approaches using network theory, allows for a holistic understanding of functional similarities amongst diseases. Here we propose a co-expression based, network theoretic approach to extract functional similarities from 20 heterogeneous diseases comprising of dystrophinopathies, inflammatory myopathies, neuromuscular, and muscle metabolic diseases. Utilizing this framework we identified seven closely associated disease clusters with 20 disease pairs exhibiting significant correlation (
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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.