TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model

• DOI: 10.1093/bioinformatics/btac200
• Publication Date: 2022-04-07T13:43:36Z
• Authors: Ke Yan; Hongwu Lv; Yichen Guo; Yongyong Chen; Hao Wu; Bin Liu
• Keywords: Sequence (biology)

Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different types. However, these focus on identifying some specific types peptides, failing predict comprehensive peptides. Moreover, it still challenging utilize properties peptides.In this study, an adaptive multi-view based tensor learning framework TPpred-ATMV proposed predicting constructs class probability information various sequence features. We constructed latent subspace among features auto-weighted model high correlation Experimental results showed that better than or highly comparable with other state-of-the-art eight peptides.The code accessed at: https://github.com/cokeyk/TPpred-ATMV.Supplementary data are available at Bioinformatics online.