43 results on '"Li, Fuyi"'
Search Results
2. ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction
3. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
4. TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters
5. ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species
6. ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning
7. VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants
8. COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants
9. Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations
10. DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions
11. RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins
12. iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
13. ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning
14. Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction
15. Positive-unlabeled learning in bioinformatics and computational biology: a brief review
16. Porpoise: a new approach for accurate prediction of RNA pseudouridine sites
17. Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations
18. HEAL: an automated deep learning framework for cancer histopathology image analysis
19. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides
20. iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
21. Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules
22. Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification
23. Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks
24. DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases
25. DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites
26. Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions
27. Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features
28. PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs
29. Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework
30. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction
31. Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
32. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites
33. PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact
34. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
35. MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
36. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms
37. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites
38. Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods
39. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome
40. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
41. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences
42. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy
43. GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome
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