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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

iTSP-PseAAC: Identifying Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC

Author(s): Muhammad Awais*, Waqar Hussain, Nouman Rasool and Yaser Daanial Khan

Volume 16, Issue 5, 2021

Published on: 08 January, 2021

Page: [700 - 709] Pages: 10

DOI: 10.2174/1574893615666210108094431

Price: $65

Abstract

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 and cross-validation.

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.

Keywords: Tumor suppressor proteins, prediction, PseAAC, statistical moments, neural network, tumor.

Graphical Abstract

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