ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

ABSTRACT: Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combini

SUBMITTER: Ahmed S 

PROVIDER: S-EPMC8654959 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature