ArticlePloS one2012
Hepatitis C virus network based classification of hepatocellular cirrhosis and carcinoma.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 55 citations in OpenAlex.
- Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.Diagnostics (Basel, Switzerland) · 2023Article
- Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.Scientific reports · 2023Article
- Uncovering the Pharmacological Mechanisms of Gexia-Zhuyu Formula (GXZY) in Treating Liver Cirrhosis by an Integrative Pharmacology Strategy.Frontiers in pharmacology · 2022Article
- Discovery of vanoxerine dihydrochloride as a CDK2/4/6 triple-inhibitor for the treatment of human hepatocellular carcinoma.Molecular medicine (Cambridge, Mass.) · 2021Article
- iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.The Journal of membrane biology · 2016Article
- iACP: a sequence-based tool for identifying anticancer peptides.Oncotarget · 2016Article
- Genetic differences among ethnic groups.BMC genomics · 2015Article
- Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.Molecular genetics and genomics : MGG · 2015Article
- Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Molecular genetics and genomics : MGG · 2015Article
- MicroRNAs as predictor markers for response to interferon treatment of chronic hepatitis C genotype-4 in Egyptian patients.PloS one · 2015Article
- iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.International journal of molecular sciences · 2014Article
- iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.International journal of molecular sciences · 2014Article
- Finding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactions.PloS one · 2014Article
- Identification of Influenza A/H7N9 virus infection-related human genes based on shortest paths in a virus-human protein interaction network.BioMed research international · 2014Article
- iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels.BioMed research international · 2014Article
- Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.BioMed research international · 2014Article
- Advanced systems biology methods in drug discovery and translational biomedicine.BioMed research international · 2013Review
- Naïve Bayes classifier with feature selection to identify phage virion proteins.Computational and mathematical methods in medicine · 2013Article
- Signal propagation in protein interaction network during colorectal cancer progression.BioMed research international · 2013Article
- Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS.BioMed research international · 2013Article
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis C virus (HCV) is a main risk factor for liver cirrhosis and hepatocellular carcinoma, particularly to those patients with chronic liver disease or injury. The similar etiology leads to a high correlation of the patients suffering from the disease of liver cirrhosis with those suffering from the disease of hepatocellular carcinoma. However, the biological mechanism for the relationship between these two kinds of diseases is not clear. The present study was initiated in an attempt to investigate into the HCV infection protein network, in hopes to find good biomarkers for diagnosing the two diseases as well as gain insights into their progression mechanisms. To realize this, two potential biomarker pools were defined: (i) the target genes of HCV, and (ii) the between genes on the shortest paths among the target genes of HCV. Meanwhile, a predictor was developed for identifying the liver tissue samples among the following three categories: (i) normal, (ii) cirrhosis, and (iii) hepatocellular carcinoma. Interestingly, it was observed that the identification accuracy was higher with the tissue samples defined by extracting the features from the second biomarker pool than that with the samples defined based on the first biomarker pool. The identification accuracy by the jackknife validation for the between-genes approach was 0.960, indicating that the novel approach holds a quite promising potential in helping find effective biomarkers for diagnosing the liver cirrhosis disease and the hepatocellular carcinoma disease. It may also provide useful insights for in-depth study of the biological mechanisms of HCV-induced cirrhosis and hepatocellular carcinoma.
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Registered trials
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