ArticlePloS one2011
Prediction of body fluids where proteins are secreted into based on protein interaction network.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 45 citations in OpenAlex.
- Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification.Molecules (Basel, Switzerland) · 2023Article
- HBFP: a new repository for human body fluid proteome.Database : the journal of biological databases and curation · 2021Article
- Human body-fluid proteome: quantitative profiling and computational prediction.Briefings in bioinformatics · 2021Review
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein-Protein Interaction Network.International journal of molecular sciences · 2017Article
- Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.BioMed research international · 2015Article
- Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores.BioMed research international · 2013Article
- QSAR classification of metabolic activation of chemicals into covalently reactive species.Molecular diversity · 2012Article
- Study of drug function based on similarity of pathway fingerprint.Protein & cell · 2012Article
- Article
- Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.PloS one · 2012Article
- Hepatitis C virus network based classification of hepatocellular cirrhosis and carcinoma.PloS one · 2012Article
- SySAP: a system-level predictor of deleterious single amino acid polymorphisms.Protein & cell · 2012Article
- Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.PloS one · 2012Article
- 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors.International journal of molecular sciences · 2011Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.
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