Yi Xiong
Orcid: 0000-0003-2910-6725Affiliations:
- Shanghai Jiao Tong University, Shanghai, China
According to our database1,
Yi Xiong
authored at least 31 papers
between 2011 and 2023.
Collaborative distances:
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Bibliography
2023
J. Chem. Inf. Model., December, 2023
LS-MolGen: Ligand-and-Structure Dual-Driven Deep Reinforcement Learning for Target-Specific Molecular Generation Improves Binding Affinity and Novelty.
J. Chem. Inf. Model., July, 2023
Genom. Proteom. Bioinform., April, 2023
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data.
Briefings Bioinform., March, 2023
Briefings Bioinform., January, 2023
2022
A transformer-based model to predict peptide-HLA class I binding and optimize mutated peptides for vaccine design.
Nat. Mach. Intell., 2022
DeepPSE: Prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism.
Comput. Biol. Medicine, 2022
T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model.
Briefings Bioinform., 2022
scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding.
Briefings Bioinform., 2022
MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.
Briefings Bioinform., 2022
2021
NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.
Nucleic Acids Res., 2021
MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information.
Comput. Biol. Medicine, 2021
MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs.
Briefings Bioinform., 2021
NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.
Briefings Bioinform., 2021
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.
Briefings Bioinform., 2021
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.
Briefings Bioinform., 2021
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.
Briefings Bioinform., 2021
2020
LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions.
Comput. Biol. Chem., 2020
iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots.
BMC Bioinform., 2020
2019
NetGO: improving large-scale protein function prediction with massive network information.
Nucleic Acids Res., 2019
Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method.
J. Chem. Inf. Model., 2019
2018
Protein-protein interface hot spots prediction based on a hybrid feature selection strategy.
BMC Bioinform., 2018
BMC Bioinform., 2018
GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank.
Bioinform., 2018
dbAMEPNI: a database of alanine mutagenic effects for protein-nucleic acid interactions.
Database J. Biol. Databases Curation, 2018
2015
Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.
Bioinform., 2015
2013
Predicting immunogenic T-cell epitopes by combining various sequence-derived features.
Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013
2011
Prediction of conformational B-cell epitopes from 3D structures by random forest with a distance-based feature.
BMC Bioinform., 2011
Prediction of Heme Binding Sites in Heme Proteins Using an Integrative Sequence Profile Coupling Evolutionary Information with Physicochemical Properties.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2011