Towards retraining-free RNA modification prediction with incremental learning.

StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.

Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction.

CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only.

MolCAP: Molecular Chemical reActivity Pretraining and prompted-finetuning enhanced molecular representation learning.

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications.

PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.

DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning.

MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning.

Predicting protein-peptide binding residues via interpretable deep learning.

Accelerating bioactive peptide discovery via mutual information-based meta-learning.

iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.