Deep attention based Proto-oncogene prediction and Oncogene transition possibility detection using moments and position based amino acid features

The loss of the regulatory function of tumor suppression genes and mutations in Proto-oncogene are the common underlying mechanisms for uncontrolled tumor growth in the varied complex of disorders known as cancer. Oncogene can be curable by means of diagnosing and treating the possibilities of Proto-oncogene at earlier stages. Recently, machine learning approaches helps to focus and provide information about the possibilities of Proto-oncogene that may change into oncogene in different cancer types. This study helps to diagnose the possibilities of Proto-oncogene which are possible to change oncogenes at earlier stage. Thus, this present study proposed an efficient unique predictor of Protooncogene with the help of Bi-Directional Long Short Term Memory added with attention concept. This approach also find the probability of Proto-oncogene to oncogene using statistical moments, position based amino-acid composition representation and deep features extracted from the sequence. Consequently, this study suggests that using a K-Nearest Neighbor classifier it is possible to find probability of changing from Proto-oncogene to cancerous oncogene.