IDENTIFICATION OF SEQUENCE BASE PROTO ONCOGENES BY INTEGRATION OF STATISTICAL MOMENTS INTO PseAAC
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Date
2019
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UMT, Lahore
Abstract
Proto-oncogenes are a group of genes that cause normal cells to become cancerous when they are mutated. Proto-oncogenes encode proteins that function to stimulate cell division, inhibit cell differentiation, and prevent cell death. While the prediction of the proto-oncogene may happen at different phases of the cancer-causing processes, the method of prediction is always a question. Prediction through in vitro experimentations is considered sometimes a standard procedure, but is very time taking, laborious and costly. This problem can be address by opting computer aided approaches i.e. bioinformatics and computational biology. Keeping this in mind, an effective new method is proposed in this study for the prediction of proto-oncogenes. The predictor proposed in this study calculates statistical moments and position-based features and incorporates them in PseAAC by using the Chou’s 5-step rules. Later on, Random Forest is used as classifier for the accurate prediction of results. The method was validated using the 10-Fold cross-validation, Jackknife testing, Self-Consistency and Independent testing, giving 95.44%, 95.21%, 97.38%, and 96.41% accurate results, respectively. These results depict that the proposed model can play a key role in the prediction of proto-oncogenes to aid the scientists in discovery of mechanism against cancer.