English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Anticancer peptides (ACPs) are a class of molecules that have gainedsignificant attention in the field of cancer research and therapy. ACPs areshort chains of amino acids, the building blocks of proteins, and they possessthe ability to selectively target and kill cancer cells. One of the keyadvantages of ACPs is their ability to selectively target cancer cells whilesparing healthy cells to a greater extent. This selectivity is often attributedto differences in the surface properties of cancer cells compared to normalcells. That is why ACPs are being investigated as potential candidates forcancer therapy. ACPs may be used alone or in combination with other treatmentmodalities like chemotherapy and radiation therapy. While ACPs hold promise asa novel approach to cancer treatment, there are challenges to overcome,including optimizing their stability, improving selectivity, and enhancingtheir delivery to cancer cells, continuous increasing in number of peptidesequences, developing a reliable and precise prediction model. In this work, wepropose an efficient transformer-based framework to identify anticancerpeptides for by performing accurate a reliable and precise prediction model.For this purpose, four different transformer models, namely ESM, ProtBert,BioBERT, and SciBERT are employed to detect anticancer peptides from amino acidsequences. To demonstrate the contribution of the proposed framework, extensiveexperiments are carried on widely-used datasets in the literature, two versionsof AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage ofproposed model enhances classification accuracy when compared to thestate-of-the-art studies. The proposed framework, ESM, exhibits 96.45 ofaccuracy for AntiCp2 dataset, 97.66 of accuracy for cACP-DeepGram dataset, and88.51 of accuracy for ACP-740 dataset, thence determining new state-of-the-art.

ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach