110 results on '"Gao, Ying-Lian"'
Search Results
2. A Locality-Constrained Linear Coding-Based Ensemble Learning Framework for Predicting Potentially Disease-Associated MiRNAs
3. MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction
4. Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases
5. MKGSAGE: A Computational Framework via Multiple Kernel Fusion on GraphSAGE for Inferring Potential Disease-Related Microbes
6. GRPGAT: Predicting CircRNA-disease Associations Based on Graph Random Propagation Network and Graph Attention Network
7. SLGCN: Structure-enhanced line graph convolutional network for predicting drug-disease associations
8. Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification
9. MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label Propagation
10. A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug–Disease Associations
11. Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning
12. MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder
13. Robust Graph Regularized Extreme Learning Machine Auto Encoder and Its Application to Single-Cell Samples Classification
14. DSNPCMF: Predicting MiRNA-Disease Associations with Collaborative Matrix Factorization Based on Double Sparse and Nearest Profile
15. BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks
16. Dual Sparse Collaborative Matrix Factorization Method Based on Gaussian Kernel Function for Predicting LncRNA-Disease Associations
17. NTBiRW: A Novel Neighbor Model Based on Two-Tier Bi-Random Walk for Predicting Potential Disease-Related Microbes
18. Non-negative low-rank representation with similarity correction for cell type identification in scRNA-seq data
19. BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data
20. Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction
21. Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data
22. HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder
23. An integrated Extreme learning machine based on kernel risk-sensitive loss of q-Gaussian and voting mechanism for sample classification
24. Predicting LncRNA-Disease Associations Based on LncRNA-MiRNA-Disease Multilayer Association Network and Bipartite Network Recommendation
25. Tensor Decomposition Based on Global Features and Sparse Structure for Analyzing Cancer Multiomics Data
26. BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix
27. A new framework for drug–disease association prediction combing light-gated message passing neural network and gated fusion mechanism
28. Multi-similarity fusion-based label propagation for predicting microbes potentially associated with diseases
29. Robust Principal Component Analysis Based On Hypergraph Regularization for Sample Clustering and Co-Characteristic Gene Selection
30. NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation
31. Tensor decomposition based on the potential low-rank and p-shrinkage generalized threshold algorithm for analyzing cancer multiomics data
32. A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection
33. LDCMFC: Predicting Long Non-coding RNA and Disease Association Using Collaborative Matrix Factorization based on Correntropy
34. Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization
35. MSF-LRR: Multi-similarity Information Fusion through Low-Rank Representation to Predict Disease-associated Microbes
36. A Simple Review of Sparse Principal Components Analysis
37. Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data
38. Adaptive total-variation joint learning model for analyzing single cell RNA seq data
39. Robust Tensor Method Based on Correntropy and Tensor Singular Value Decomposition for Cancer Genomics Data
40. Sparse Hyper-graph Non-negative Matrix Factorization by Maximizing Correntropy
41. Ensemble Adaptive Total Variation Graph Regularized NMF for Singlecell RNA-seq Data Analysis
42. Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification
43. A Two-Stage Sparse Selection Method for Extracting Characteristic Genes
44. Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection
45. Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes
46. Semi-supervised Feature Extraction for RNA-Seq Data Analysis
47. Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification
48. Kernel risk-sensitive mean p-power error based robust extreme learning machine for classification
49. DSTPCA: Double-Sparse Constrained Tensor Principal Component Analysis Method for Feature Selection
50. Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data
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