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Fig. 1 | Journal of Translational Medicine

Fig. 1

From: Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks

Fig. 1

Flow diagram. CHB-, cirrhosis-, and HCC-associated networks were constructed using disease-associated genes downloaded from OMIM. Functional modules were identified using the MCODE algorithm. Then, the results of module identification were optimized based on the minimum entropy criterion. The enrichment analysis of KEGG pathways was performed with DAVID 6.7 software. The similarity between modules was calculated using SimiNEF. Four AMs (DEMs, CAMs, TAMs, and OAMs) were identified. The relationships between OAM genes and HCC were validated by published literature. AMs allosteric modules, DEMs disease-exclusive modules, CAMs conserved allosteric modules, TAMs transitional allosteric modules, and OAMs oncogenic allosteric modules. ‘√’ or ‘×’ represents its appearance ‘yes’ or ‘no’ in the group, respectively. For example, the module is identified as ‘conserved’ when it is found both in CHB and cirrhosis, cirrhosis and HCC, CHB and HCC, or among the three groups (‘√’), and Sne = 100%

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