An Accurate Bioinformatics Tool For Anti-Cancer Peptide Generation Through Deep Learning Omics

Abstract

The Anti-cancer targets play crucial role in signalling processes of cells. We have developed an Anti-Cancer Scanner (ACS) tool for identification of Anti-cancer targets in form of peptides. ACS tool also allows fast fingerprinting of the Anti-cancer targets of significance in the current bioinformatics research. There are tools currently available which predicts the above-mentioned features in single platform. In the present work, we have compared the features predicted by ACS with other on-line available methods and evaluated the performance of the ACS tool. ACS scanned the Anti-cancer target protein sequences provided by the user against the Anti-cancer target data-sets. It has been developed in PERL language and it is scalable having an extensible application in bioinformatics with robust coding architecture. It achieves a prediction accuracy of 95%, which is much higher than the existing tools.