ArticleBioMed research international2016
Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine.International journal of molecular sciences · 2022Review
- Comparative bioinformatical analysis of pancreatic head cancer and pancreatic body/tail cancer.Medical oncology (Northwood, London, England) · 2020Article
- TBX2 Identified as a Potential Predictor of Bone Metastasis in Lung Adenocarcinoma via Integrated Bioinformatics Analyses and Verification of Functional Assay.Journal of Cancer · 2020Article
- Hub Genes and Key Pathway Identification in Colorectal Cancer Based on Bioinformatic Analysis.BioMed research international · 2019Article
- Article
- Multiple kernels learning-based biological entity relationship extraction method.Journal of biomedical semantics · 2017Article
- Predicting disease-related genes using integrated biomedical networks.BMC genomics · 2017Article
- Prediction and Validation of Hub Genes Associated with Colorectal Cancer by Integrating PPI Network and Gene Expression Data.BioMed research international · 2017Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence.
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Registered trials
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