ReviewCurrent opinion in systems biology2019
Footprint-based functional analysis of multiomic data.
Review in Current opinion in systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 62 citations in OpenAlex.
- Complex assembly and activity states as multifaceted protein attributes explaining phenotypic variability.Molecular systems biology · 2026Article
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- MultiOmicsXplorer, a tool to browse, access and analyse multi-omics data.BMC bioinformatics · 2026Article
- Multi-Omics Guided Pathway and Network Analysis of Clinical Metabolomics and Proteomics Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- InCURA: integrative gene clustering based on transcription factor binding sites.Nucleic acids research · 2025Article
- A gene expression atlas of a juvenile nervous system.bioRxiv : the preprint server for biology · 2025Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- Patient-Specific Regulatory Network Rewiring in Inflammatory Bowel Disease: How Genetic Polymorphisms Divert Incoming Signals and Contribute to Disease Pathogenesis.Inflammatory bowel diseases · 2025Article
- Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability.bioRxiv : the preprint server for biology · 2025Article
- Dynamic multi-omics and mechanistic modeling approach uncovers novel mechanisms of kidney fibrosis progression.Molecular systems biology · 2025Article
- RIDDEN: Data-driven inference of receptor activity from transcriptomic data.PLoS computational biology · 2025Article
- Comprehensive evaluation of phosphoproteomic-based kinase activity inference.Nature communications · 2025Article
- Article
- SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks.NPJ systems biology and applications · 2024Article
- Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion.iScience · 2024Article
- Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.Cell reports methods · 2024Article
- Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition.Molecular systems biology · 2024Article
- Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system.Nature immunology · 2024Review
- Exploring common pathogenic association between Epstein Barr virus infection and long-COVID by integrating RNA-Seq and molecular dynamics simulations.Frontiers in immunology · 2024Article
- Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities.Nucleic acids research · 2023Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms.
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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.