Pattern discovery and cancer gene identification in integrated cancer genomic data

Cited by (475)

Items 201–300 of 475

1. Translating pharmacogenomic sequencing data into drug response predictions—How to interpret variants of unknown significance
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2. Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data
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3. Multi-Omics Profiling of the Tumor Microenvironment
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4. Single nucleotide polymorphism array profiling identifies distinct chromosomal aberration patterns across colorectal adenomas and carcinomas
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5. Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction
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6. Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model
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7. Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies
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8. Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma
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9. Identifying Cancer Patient Subgroups by Finding Co-Modules From the Driver Mutation Profiles and Downstream Gene Expression Profiles
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10. Estimation of total mediation effect for high-dimensional omics mediators
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11. Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data
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12. Immunoproteomics: A Review of Techniques, Applications, and Advancements
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13. Intrinsic-dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing
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14. Integrative and regularized principal component analysis of multiple sources of data
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15. High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis
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16. A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data
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17. Integrated Multi-Omics Maps of Lower-Grade Gliomas
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18. Network-based analysis for identification of candidate genes for colorectal cancer progression.
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19. Personalized Disease Phenotypes from Massive OMICs Data
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20. I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms
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21. Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets
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22. MOPA: An integrative multi-omics pathway analysis method for measuring omics activity
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23. Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
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24. Efficient Drug-Pathway Association Analysis via Integrative Penalized Matrix Decomposition
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25. Integrated Cancer Subtyping using Heterogeneous Genome-Scale Molecular Datasets
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26. MCNF: A Novel Method for Cancer Subtyping by Integrating Multi-Omics and Clinical Data
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27. Navigating multi-omic integration methods for human microbiome research
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28. Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery
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29. An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients
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30. Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis
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31. Multiomics in cancer biomarker discovery and cancer subtyping
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32. BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data
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33. SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis
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34. Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways
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35. 2D–EM clustering approach for high-dimensional data through folding feature vectors
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36. Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer
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37. Integrated Analysis of Copy Number Variation, Microsatellite Instability, and Tumor Mutation Burden Identifies an 11-Gene Signature Predicting Survival in Breast Cancer
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38. Hierarchical autoencoder-based multi-omics subtyping and prognosis prediction framework for lung adenocarcinoma
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39. ‘Big data’ approaches for novel anti-cancer drug discovery
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40. Heterofusion: Fusing genomics data of different measurement scales
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41. A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort
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42. MSPL: Multimodal Self-Paced Learning for Multi-Omics Feature Selection and Data Integration
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43. Vertical integration methods for gene expression data analysis
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44. IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration
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45. DeePROG: Deep Attention-Based Model for Diseased Gene Prognosis by Fusing Multi-Omics Data
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46. Integrative Analysis Identifies Multi-Omics Signatures That Drive Molecular Classification of Uveal Melanoma
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47. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival
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48. Analysis of Individual Protein Regions Provides Novel Insights on Cancer Pharmacogenomics
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49. Low-Rank Joint Subspace Construction for Cancer Subtype Discovery
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50. Simultaneous non-Gaussian component analysis (SING) for data integration in neuroimaging
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51. Generalized simultaneous component analysis of binary and quantitative data
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52. Generative Models Utilizing Padding Can Efficiently Integrate and Generate Multi-Omics Data
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53. Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery
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54. Epigenetic Responses to Nonchemical Stressors: Potential Molecular Links to Perinatal Health Outcomes
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55. A Meta-Analysis Approach for Characterizing Pan-Cancer Mechanisms of Drug Sensitivity in Cell Lines
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56. MOUSSE: Multi-Omics Using Subject-Specific SignaturEs
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57. iOmicsPASS: a novel method for integration of multi-omics data over biological networks and discovery of predictive subnetworks
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58. Integrative molecular subtypes of acute myeloid leukemia
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59. PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data
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60. I-SVVS: integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data
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61. Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering
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62. Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis
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65. Machine Learning Approaches for Multi-omics Data Integration in Medicine
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68. The DACH1 gene is frequently deleted in prostate cancer, restrains prostatic intraepithelial neoplasia, decreases DNA damage repair, and predicts therapy responses
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69. PAMOGK: A Pathway Graph Kernel based Multi-Omics Clustering Approach for Discovering Cancer Patient Subgroups
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70. Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data
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71. Integrative Analysis of Multimodal Omics Data
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72. Identifying subgroups of childhood obesity by using multiplatform metabotyping
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73. Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma
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74. Unravelling disease complexity: integrative analysis of multi-omic data in clinical research
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76. Semi-supervised identification of cancer subgroups using survival outcomes and overlapping grouping information
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77. HISMD: A Novel Immune Subtyping System for HNSCC
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78. Comparison of Gene Selection Methods for Clustering Single-cell RNA-seq Data
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79. HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol
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80. Mass spectrometry for mitochondrial multi-omics
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81. Machine learning for image-based multi-omics analysis of leaf veins
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82. Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets
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83. Identification of Tumor Mutation Burden, Microsatellite Instability, and Somatic Copy Number Alteration Derived Nine Gene Signatures to Predict Clinical Outcomes in STAD
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84. Mass Spectrometry-Based Metabolomics in Pediatric Health and Disease
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85. Subtype-MMCC: multimodal contrastive clustering approach for cancer subtype discovery with multi-omics data
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87. Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data
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89. A Bayesian framework for pathway‐guided identification of cancer subgroups by integrating multiple types of genomic data
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92. Bi-EB: Empirical Bayesian Biclustering for Multi-Omics Data Integration Pattern Identification among Species
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93. Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples
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94. Semiring Rank Matrix Factorization
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95. MGKA: A genetic algorithm-based clustering technique for genomic data
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96. Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations
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97. Patient Similarity Network of Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic and Clinical Features
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98. playOmics: A multi-omics pipeline for interpretable predictions and biomarker discovery
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99. A Comprehensive Protocol and Step-by-Step Guide for Multi-Omics Integration in Biological Research
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100. NESM: a network embedding method for tumor stratification by integrating multi-omics data
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