Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.05.327015v1?rss=1 Authors: Wert-Carvajal, C., Sanchez-Garcia, R., Macias, J. R., Sanz-Pamplona, R., Mendez Perez, A., Alemany, R., Veiga Chacon, E., Sorzano, C. O. S., Munoz-Barrutia, A. Abstract: Motivation: Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope immunogenity predictions in mice. Results: We have developed a web server tool (NAP-CNB), based on recurrent neural networks, able to predict putative neoantigens of a tumor. The developed software is able to estimate H-2 peptide ligands with an AUC of 0.95. As a proof-of-concept, we used the B16 melanoma model to test the predictive capabilities of the system and we report its putative neoantigens. Copy rights belong to original authors. Visit the link for more info