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TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.

, , , , , und . J. Comput. Aided Mol. Des., 34 (8): 841-856 (2020)

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DeepTF: Accurate Prediction of Transcription Factor Binding Sites by Combining Multi-scale Convolution and Long Short-Term Memory Neural Network., , und . IScIDE (2), Volume 11936 von Lecture Notes in Computer Science, Seite 126-138. Springer, (2019)Protein-protein interaction sites prediction by ensembling SVM and sample-weighted random forests., , , , und . Neurocomputing, (2016)DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies., , , , , , und . IEEE ACM Trans. Comput. Biol. Bioinform., 19 (5): 2749-2759 (2022)Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs., , , , , und . IEEE ACM Trans. Comput. Biol. Bioinform., 14 (6): 1389-1398 (2017)PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection., , , , , und . Briefings Bioinform., (2021)Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling., , , , , und . Neurocomputing, (2013)Enhancing Protein-ATP and Protein-ADP Binding Sites Prediction Using Supervised Instance-Transfer Learning., , und . ACPR, Seite 759-763. IEEE Computer Society, (2017)Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues., , , , , , und . Bioinform., (Dezember 2023)Efficient and Robust TWSVM Classifier Based on L1-Norm Distance Metric for Pattern Classification., , , und . ACPR, Seite 436-441. IEEE Computer Society, (2017)TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree., , , , , und . J. Comput. Aided Mol. Des., 34 (8): 841-856 (2020)