ArticleNucleic acids research2012
SteinerNet: a web server for integrating 'omic' data to discover hidden components of response pathways.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration.Scientific reports · 2026Article
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Graphery: interactive tutorials for biological network algorithms.Nucleic acids research · 2021Article
- Network analyses in microbiome based on high-throughput multi-omics data.Briefings in bioinformatics · 2021Review
- Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing.Nature communications · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach.Scientific reports · 2020Article
- ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues.Nucleic acids research · 2019Article
- Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data.Genome medicine · 2019Article
- Article
- EviNet: a web platform for network enrichment analysis with flexible definition of gene sets.Nucleic acids research · 2018Article
- The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways.BMC bioinformatics · 2017Article
- PCSF: An R-package for network-based interpretation of high-throughput data.PLoS computational biology · 2017Article
- HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks.Nucleic acids research · 2017Article
- Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.Cancer research · 2016Article
- A convex optimization approach for identification of human tissue-specific interactomes.Bioinformatics (Oxford, England) · 2016Article
- Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.PLoS computational biology · 2016Review
- A survey of best practices for RNA-seq data analysis.Genome biology · 2016Review
- Methods for the integration of multi-omics data: mathematical aspects.BMC bioinformatics · 2016Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
High-throughput technologies including transcriptional profiling, proteomics and reverse genetics screens provide detailed molecular descriptions of cellular responses to perturbations. However, it is difficult to integrate these diverse data to reconstruct biologically meaningful signaling networks. Previously, we have established a framework for integrating transcriptional, proteomic and interactome data by searching for the solution to the prize-collecting Steiner tree problem. Here, we present a web server, SteinerNet, to make this method available in a user-friendly format for a broad range of users with data from any species. At a minimum, a user only needs to provide a set of experimentally detected proteins and/or genes and the server will search for connections among these data from the provided interactomes for yeast, human, mouse, Drosophila melanogaster and Caenorhabditis elegans. More advanced users can upload their own interactome data as well. The server provides interactive visualization of the resulting optimal network and downloadable files detailing the analysis and results. We believe that SteinerNet will be useful for researchers who would like to integrate their high-throughput data for a specific condition or cellular response and to find biologically meaningful pathways. SteinerNet is accessible at http://fraenkel.mit.edu/steinernet.
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