ArticleNPJ systems biology and applications2025
Multi-omic network inference from time-series data.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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
9 citing papers in PubMed.
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- Review
- Toward A Pre-disease State-centered New Paradigm in Multi-omics Research.Genomics, proteomics & bioinformatics · 2026Article
- The new frontier of newborn screening: integrating genomics, metabolomics, and data science for inborn errors of metabolism.Orphanet journal of rare diseases · 2026Review
- Histone mark remodeling in cancer: an enhancer-centered perspective.Genes & genomics · 2026Review
- Decoding cellular population dynamics through mechanistic modelling and statistical data analysis.NPJ systems biology and applications · 2026Review
- NetCrafter: ontology-derived gene network modeling and functional interpretation.Briefings in bioinformatics · 2026Article
- Epitranscriptomics, immunometabolism, and NETosis: unlocking novel mechanisms of traditional Chinese medicine in COPD.Frontiers in immunology · 2026Review
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these regulatory networks have primarily focused on single-omic studies, overlooking inter-layer regulatory relationships. To address these limitations, we developed MINIE, a computational method that integrates multi-omic data from bulk metabolomics and single-cell transcriptomics through a Bayesian regression approach that explicitly models the timescale separation between molecular layers. We validate the method on both simulated datasets and experimental Parkinson's disease data. MINIE exhibits accurate and robust predictive performance across and within omic layers, including curated multi-omic networks and the lac operon. Benchmarking demonstrated significant improvements over state-of-the-art methods while ranking among the top performers in comprehensive single-cell network inference analysis. The integration of regulatory dynamics across molecular layers and temporal scales provides a powerful tool for comprehensive multi-omic network inference.
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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.