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Prediction of HPV Risk Types from Protein Secondary Structure

Identification of high risk among viruses is crucial for interpreting its oncogenic mechanisms. Also it is gainful in developing ultra modern clinical tools for its diagnosis, prevention and treatment. Prediction of the structure of a particular virus helps to classify the risk type of that virus. Prediction methods based on text based classification do not provide satisfactory results as compared to sequence-based techniques. In particular, text-mining approaches only relay on the documentation procured from the studies. Hence insufficiency of the evidences for an unknown virus gives rise to classification issues. Therefore risk prediction of viruses from protein sequence and its structure is an effective alternative to text based classification. In this work, Machine learning techniques are used for risk type classification of Papillomaviruses which is having high potentials to cause cancer.