BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
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Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. New insights into the pathogenesis of this lethal disease are urgently needed. Chromosomal copy number alterations (CNAs) can lead to activation of oncogenes and inactivation of tumor suppressors in human cancers. Thus, identification of cancer-specific CNAs will not only provide new insight into understanding the molecular basis of tumor genesis but also facilitate the identification of HCC biomarkers using CNA. This paper presents the TMT-HCC system; it is a tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification. TMT-HCC provides researchers with a powerful way to identify and discern molecular biomarkers of HCC to inform diagnosis, prognosis, and treatment driver genes with causal roles in carcinogenesis is to detect genomic regions that under frequent alterations in cancers (CNAs). TMT-HCC also extracts protein-protein interactions from the full text of the scientific papers. The results provided that the integration of genomic and transcriptional data offers powerful potential for identifying novel cancer genes in HCC pathogenesis.