ReviewGenes2019
Biological Network Approaches and Applications in Rare Disease Studies.
Review in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Integrative Transcriptomic and Network-Based Analysis of Neuromuscular Diseases.International journal of molecular sciences · 2025Article
- DeepGraphMut: a graph-based deep learning method for cancer prognosis using somatic mutation profile.Briefings in bioinformatics · 2025Article
- Prioritizing FDA approved therapeutics for treating sepsis phenotypes: A network modeling approach based on neutrophil proteomics.Frontiers in immunology · 2025Article
- In Silico Modeling of Fabry Disease Pathophysiology for the Identification of Early Cellular Damage Biomarker Candidates.International journal of molecular sciences · 2024Article
- Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients.International journal of molecular sciences · 2024Article
- GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.BMC bioinformatics · 2024Article
- Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.Biomolecules · 2024Article
- Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.bioRxiv : the preprint server for biology · 2023Article
- Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome.Orphanet journal of rare diseases · 2023Article
- Uncovering Protein Networks in Cardiovascular Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
- NetSHy: network summarization via a hybrid approach leveraging topological properties.Bioinformatics (Oxford, England) · 2023Article
- Current Insights into miRNA and lncRNA Dysregulation in Diabetes: Signal Transduction, Clinical Trials and Biomarker Discovery.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Targeted RNAseq Improves Clinical Diagnosis of Very Early-Onset Pediatric Immune Dysregulation.Journal of personalized medicine · 2022Article
- Omics of endothelial cell dysfunction in sepsis.Vascular biology (Bristol, England) · 2022Review
- A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.Briefings in bioinformatics · 2021Article
- A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity.American journal of human genetics · 2021Article
- Assembling Disease Networks From Causal Interaction Resources.Frontiers in genetics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.
Indexed as
Identifiers
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.