ArticlePLoS computational biology2012
A network-based approach for predicting missing pathway interactions.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic.Cancers · 2021Article
- An Integrated Bioinformatics Study of a Novel Niclosamide Derivative, NSC765689, a PotentialInternational journal of molecular sciences · 2021Article
- Reconstructing signaling pathways using regular language constrained paths.Bioinformatics (Oxford, England) · 2019Article
- The Protein-Protein Interaction Network ofFrontiers in physiology · 2019Article
- Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models.F1000Research · 2019Article
- Identification of Biomarkers Based on Differentially Expressed Genes in Papillary Thyroid Carcinoma.Scientific reports · 2018Article
- Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.Molecular biology and evolution · 2017Article
- Network-Based Analysis of eQTL Data to Prioritize Driver Mutations.Genome biology and evolution · 2016Article
- PheNetic: network-based interpretation of molecular profiling data.Nucleic acids research · 2015Article
- Systems Medicine: from molecular features and models to the clinic in COPD.Journal of translational medicine · 2014Article
- Signaling hypergraphs.Trends in biotechnology · 2014Article
- The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis.BMC systems biology · 2014Article
- Integrating PPI datasets with the PPI data from biomedical literature for protein complex detection.BMC medical genomics · 2014Article
- PPI network analysis of mRNA expression profile of ezrin knockdown in esophageal squamous cell carcinoma.BioMed research international · 2014Article
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3 authors.
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Abstract
Embedded within large-scale protein interaction networks are signaling pathways that encode response cascades in the cell. Unfortunately, even for well-studied species like S. cerevisiae, only a fraction of all true protein interactions are known, which makes it difficult to reason about the exact flow of signals and the corresponding causal relations in the network. To help address this problem, we introduce a framework for predicting new interactions that aid connectivity between upstream proteins (sources) and downstream transcription factors (targets) of a particular pathway. Our algorithms attempt to globally minimize the distance between sources and targets by finding a small set of shortcut edges to add to the network. Unlike existing algorithms for predicting general protein interactions, by focusing on proteins involved in specific responses our approach homes-in on pathway-consistent interactions. We applied our method to extend pathways in osmotic stress response in yeast and identified several missing interactions, some of which are supported by published reports. We also performed experiments that support a novel interaction not previously reported. Our framework is general and may be applicable to edge prediction problems in other domains.
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