ArticleBioinformatics (Oxford, England)2015
SAMNetWeb: identifying condition-specific networks linking signaling and transcription.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- MORE interpretable multi-omic regulatory networks to characterise phenotypes.Briefings in bioinformatics · 2025Article
- Genomics Research on the Road of Studying Biology and Virulence of Cereal Rust Fungi.Molecular plant pathology · 2025Review
- ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues.Nucleic acids research · 2019Article
- Translational Systems Pharmacology Studies in Pregnant Women.CPT: pharmacometrics & systems pharmacology · 2018Review
- Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine.Frontiers in immunology · 2018Review
- A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease.G3 (Bethesda, Md.) · 2017Article
- Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia.Integrative biology : quantitative biosciences from nano to macro · 2016Article
- Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.PLoS computational biology · 2016Article
- Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes.Genes & nutrition · 2015Article
- Genomic, Proteomic, and Metabolomic Data Integration Strategies.Biomarker insights · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
motivationHigh-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once. SUMMARY: We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment 'share' underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments. AVAILABILITY AND IMPLEMENTATION: SAMNetWeb is freely available at http://fraenkel.mit.edu/samnetweb.
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.