Background: The uncontrolled growth due to accumulation of genetic and epigenetic changes
as a result of loss or reduction in the normal function of Tumor Suppressor Genes (TSGs) and Prooncogenes
is known as cancer. TSGs control cell division and growth by repairing DNA mistakes during
replication and restrict the unwanted proliferation of a cell or activities, that are part of tumor production.
Objectives: This study aims to propose a novel, accurate, user-friendly model to predict tumor suppressor
proteins, which would be freely available to experimental molecular biologists to assist them using in
vitro and in vivo studies.
Methods: The prediction model has used the input feature vector (IFV) calculated from the physicochemical
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
Results: 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.
Conclusion: It is concluded that the proposed model for prediction of the tumor suppressor proteins can
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.