Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning

• Many approaches possible to tune the search between local optimization by exploiting the gradient of neighbor solutions and global optimization by exploring the whole.

– We need phenotype similarity as this is the ground truth representation of an individual (two individuals are similar, because they behave similar no matter how they actually look

– Verifies the current solution whether it satisfies a set of given constraints that specify allowable combinations of values for subsets of variables.. – Solution is a

A combined serum and tissue proteomic study applied to a c-Myc transgenic mouse model of hepatocellular carcinoma identified novel disease regulated proteins suitable for

In the current search for different biomarkers in canine epilepsy, the most important findings were that GFAP levels in CSF reflect severe structural changes in the brain

The inquired items included profiles of the recipients (age, sex, cause of liver cirrhosis, viral hepatitis B, viral hepatitis C, serum alpha-fetoprotein,

Keywords: immune checkpoint inhibition; hepatocellular carcinoma; tumor surveillance; immunother- apy; T cell responses; treatment failure; CAR therapy; chronic hepatitis; HBV;

Methods: To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning