ArticleJournal of proteome research2021
Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine.
Article in Journal of proteome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- MicroSSNet: an R package for microbial network construction and analysis at the single-sample and aggregated levels.BMC bioinformatics · 2026Article
- Challenges and Opportunities in Single-Sample Network Modeling.bioRxiv : the preprint server for biology · 2026Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Individual-network based predictions of microbial interaction signatures for response to biological therapies in IBD patients.Frontiers in molecular biosciences · 2024Article
- Edge and modular significance assessment in individual-specific networks.Scientific reports · 2023Article
- Capturing the dynamics of microbial interactions through individual-specific networks.Frontiers in microbiology · 2023Article
- Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and polymicrobial necrotising soft tissue infections.BMC medicine · 2022Article
- Analysis of metabolic dynamics during drought stress in Arabidopsis plants.Scientific data · 2022Article
- Defining Blood Plasma and Serum Metabolome by GC-MS.Metabolites · 2021Review
- Article
- New software tools, databases, and resources in metabolomics: updates from 2020.Metabolomics : Official journal of the Metabolomic Society · 2021Review
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite-metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.
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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.