ArticlePLoS computational biology2011
Protein networks as logic functions in development and cancer.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 98 citations in OpenAlex.
- Time-Resolved Network Entropy Analysis Identifies a CTNND1-VAV3 Subnetwork in Gefitinib-Resistant NSCLC.International journal of molecular sciences · 2026Article
- Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping.BioData mining · 2025Article
- Article
- Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.Biomolecules · 2023Article
- NetRank Recovers Known Cancer Hallmark Genes as Universal Biomarker Signature for Cancer Outcome Prediction.Frontiers in bioinformatics · 2022Article
- Determination of biomarkers from microarray data using graph neural network and spectral clustering.Scientific reports · 2021Article
- Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia.Journal of personalized medicine · 2021Article
- forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction.Bioinformatics (Oxford, England) · 2020Article
- A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.BMC medical genomics · 2019Article
- High throughput analysis to identify key gene molecules that inhibit adipogenic differentiation and promote osteogenic differentiation of human mesenchymal stem cells.Experimental and therapeutic medicine · 2019Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification.Scientific reports · 2018Article
- A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.Bioinformatics (Oxford, England) · 2018Article
- Statistical and integrative system-level analysis of DNA methylation data.Nature reviews. Genetics · 2018Review
- Systemic tracking of diagnostic function modules for post-menopausal osteoporosis in a differential co-expression network view.Experimental and therapeutic medicine · 2018Article
- Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.Indian journal of pediatrics · 2017Article
- Article
- Pathway-Based Genomics Prediction using Generalized Elastic Net.PLoS computational biology · 2016Article
- Computational systems biology in cancer brain metastasis.Frontiers in bioscience (Scholar edition) · 2016Review
- Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.International journal of molecular sciences · 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
2 authors at 1 institution in 1 country.
Funding
Abstract
Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic.
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.