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dc.contributor.authorArif, Muhammad
dc.contributor.authorKabir, Muhammad
dc.contributor.authorAhmad, Saeed
dc.contributor.authorKhan, Abid
dc.contributor.authorGe, Fang
dc.contributor.authorKhelifi, Adel
dc.contributor.author-Jun Yu, Dong
dc.date.accessioned2022-05-11T05:57:48Z
dc.date.available2022-05-11T05:57:48Z
dc.date.issued2021-08
dc.identifier.citationArif, M., Kabir, M., Ahmad, S., Khan, A., Ge, F., Khelifi, A., & Yu, D. J. (2021). DeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies. IEEE/ACM Transactions on Computational Biology and Bioinformatics.en_US
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/3390
dc.description.abstractCell-penetrating peptides (CPPs) are special kind of peptides capable of carrying variety of bioactive molecules such as genetic materials, short interfering RNA and nanoparticles into cell. In recent era, research on CPP has gained substantial interest from researchers to analyze its biological mechanisms for safe drug delivery agents and therapeutic application. Identifying CPP through traditional methods is extremely slow, overpriced and laborious, particularly due to large volume of unannotated peptide sequences accumulating in World Bank repository. To date; numerous computational methods have been developed, however, the available machine-learning tools cannot distinguish the CPPs and their uptake efficiency. This study aiming to develop two-layer deep learning framework, named DeepCPPred for identifying both CPPs in the first-phase and uptake efficiency peptides in the second-phase. The predictor first uses the four types of descriptors that cover the evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then the extracted features are optimized through elastic net algorithm and fed into cascade deep-forest for building the final CPP model. The proposed method achieved 99.45% overall accuracy on benchmark dataset in the first-layer and 95.43% accuracy in the second-layer using 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approachen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectCell-penetrating peptidesen_US
dc.subjectBioinformaticsen_US
dc.subjectFeature extractionen_US
dc.subjectFeature selectionen_US
dc.subjectDeep foresten_US
dc.titleDeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficienciesen_US
dc.title.alternativejournal Articalen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TCBB.2021.3102133


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