Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 5, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab008
Keywords
anticancer; peptide; representation learning; light gradient boosting; feature selection
Funding
- National Natural Science Foundation of China [62001090, 61922020, 61771331]
- China Postdoctoral Science Foundation [2020M673184]
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The study introduced a computational method named iACP-DRLF for identifying anticancer peptides, utilizing light gradient boosting machine algorithm and two sequence embedding technologies. Results showed that deep representation learning features significantly enhanced the models' ability to differentiate anticancer peptides.
Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, we proposed a computational method named identify anticancer peptides via deep representation learning features (iACP-DRLF) using light gradient boosting machine algorithm and deep representation learning features. TWo kinds of sequence embedding technologies were used, namely soft symmetric alignment embedding and unified representation (UniRep) embedding, both of which involved deep neural network models based on long short-term memory networks and their derived networks. The results showed that the use of deep representation learning features greatly improved the capability of the models to discriminate anticancer peptides from other peptides. Also, UMAP (uniform manifold approximation and projection for dimension reduction) and SHAP (shapley additive explanations) analysis proved that UniRep have an advantage over other features for anticancer peptide identification. The python script and pretrained models could be downloaded from https://github.com/zhibinlv/iACP-DRLF or from http://public.aibiochem.net/iACP-DRLF/.
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