A Statistical Framework for Classification of Tumor Type from microRNA Data
Hepatocellular carcinoma (HCC) is a type of liver cancer with low survival rate, not least due to the difficulty of diagnosing it in an early stage. The objective of this thesis is to build a random forest classification method based on microRNA (and messenger RNA) expression profiles from patients with HCC. The main purpose is to be able to distinguish between tumor samples and normal samples by measuring the miRNA expression. If successful, this method can be used to detect HCC at an earlier stage and to design new therapeutics. The microRNAs and messenger RNAs which have a significant difference in expression between tumor samples and normal samples are selected for building random forest classification models. These models are then tested on paired samples of tumor and surrounding normal tissue from patients with HCC. The results show that the classification models built for classifying tumor and normal samples have high prediction accuracy and hence show high potential for using microRNA and messenger RNA expression levels for diagnosis of HCC.
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