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Proteome analysis of human hepatocellular carcinoma tissues was conducted using two-dimensional difference gel electrophoresis coupled with mass spectrometry. Paired samples from the normal and tumor region of resected human liver were labeled with Cy3 and Cy5, respectively while the pooled standard sample was labeled with Cy2. After analysis by the DeCyder(More)
An integrated approach in protein discovery through the use of multidisciplinary tools was reported. A novel protein, Hcc-1, was identified by analysis of the hepatocellular carcinoma (HCC)-M cell proteome. The assembled EST sequence of the 210 amino acid novel protein was subsequently confirmed by rapid amplification of cDNA ends (RACE). A total of 687 bp(More)
Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver. Thus there is great interest to identify novel HCC diagnostic markers for early detection of the disease and tumour specific associated proteins as potential therapeutic targets in the treatment of HCC. Currently, we are screening for early biomarkers as well as studying the(More)
Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown(More)
Coenzyme M (CoM) is methylated during methanogenesis from monomethyamine in a reaction catalyzed by three proteins. Using monomethylamine, a 52-kDa polypeptide termed monomethylamine methyltransferase (MMAMT) methylates the corrinoid cofactor bound to a second polypeptide, monomethylamine corrinoid protein (MMCP). Methylated MMCP then serves as a substrate(More)
Hepatocellular carcinoma (HCC or hepatoma) is the most common primary cancer of the liver. Persistent viral infection by the hepatic B or C virus is probably the most important cause of HCC worldwide. It is responsible for approximately one million deaths each year, predominantly in the underdeveloped and developing countries, but its incidence is also on(More)
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were(More)
The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training da-tasets, which include Twitter contents of an account owner and its list of followers , using features generated in different ways for two machine(More)