2019
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Browsing 2019 by Author "Muhammad Awais"
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Item iTSP-PseAAC: Identify Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC(UMT, Lahore, 2019) Muhammad AwaisThe tumor suppressor genes (TSG), are like normal genes, controllers of cells related function from cell production to the death of the cell, if they are working properly, they can control the cell division, repairing of DNA mistakes and many other functions. There is a number of other tumor suppressor proteins that suppress the gene to not encode and produce cells. The gene, to act and perform like tumor suppression, undergo to transcription and translation process and produced the relevant proteins which bind with DNA and perform the tumor suppression activities to control the unwanted growth of cell or activities that are the part of tumor production. This study aims to propose a new and more accurate tumor suppressor proteins predictor and make it, easy to use, user-friendly and publicly available to the experimental biologist to get their desired results. The predictor model has used input features vector (IFV) calculated form the physiochemical properties of proteins based on FCNN to compute the accuracy, sensitivity, specificity, and MCC. The proposed model was validated against different exhaustive validation techniques i.e. self-consistency and cross-validation. Using self- consistency, the accuracy is 99%, for cross-validation and independent testing has 99.80% and 100% accuracy respectively. The overall accuracy of the proposed model is 99%, sensitivity value 98% and specificity 99% and F1-score was 0.99. It concludes, the proposed model for prediction of the tumor suppressor proteins has the ability to predict the tumor suppressor proteins efficiently, but it still has space for improvements in computational ways as the protein sequences may rapidly increase, day by day.