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Publications
- SCTC: inference of developmental potential from single-cell transcriptional complexity
- scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks
- scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention
- pSuc-PseRat: Predicting Lysine Succinylation in Proteins by Exploiting the Ratios of Sequence Coupling and Properties
- CITEMO XMBD : A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells
- Identifying SARS-CoV-2 infected cells with scVDN
- Cervical cytology screening using the fused deep learning architecture with attention mechanisms
- Applications of Machine Learning Methods in Drug Toxicity Prediction
- Gene function and cell surface protein association analysis based on single-cell multiomics data
- Dormant Nfatc1 reporter-marked basal stem/progenitor cells contribute to mammary lobuloalveoli formation
- Modeling and analyzing single-cell multimodal data with deep parametric inference
- RWLPAP: Random Walk for lncRNA-protein Associations Prediction
- Study on the Mechanisms of Active Compounds in Traditional Chinese Medicine for the Treatment of Influenza Virus by Virtual Screening
- The Bipartite Network Projection-Recommended Algorithm for Predicting Long Non-coding RNA-Protein Interactions
- IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction
- Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints
- LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization
- HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy
- LPI-ETSLP: lncRNA–protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction
- CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods