Bioinformatics | Cancer Research Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction. July 16, 2024
Bioinformatics | Diabetology | Pharmacology StructuralDPPIV: a novel deep learning model based on atom-structure for predicting dipeptidyl peptidase-IV inhibitory peptides. February 2, 2024
Drug Discovery | Pharmacology MolCAP: Molecular Chemical reActivity Pretraining and prompted-finetuning enhanced molecular representation learning. November 13, 2023
Bioinformatics | Immunotherapy | Oncology CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides. October 20, 2023
Drug Development | Organic Chemistry Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks. October 3, 2023
Bioinformatics PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model. August 9, 2023
Bioinformatics | Computational Biology | Molecular Biology Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications. July 29, 2023
Bioinformatics | Oncology CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only. May 30, 2023
Bioinformatics | Plant Biology ExamPle: Explainable deep learning framework for the prediction of plant small secreted peptides. March 10, 2023
Bioinformatics | Structural Biology Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. February 16, 2023
Bioinformatics | Computational Biology DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis. February 16, 2023
Bioinformatics | Machine Learning | Pharmacology SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. December 23, 2022