Pattern discovery and cancer gene identification in integrated cancer genomic data

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Items 301–400 of 475

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2. Integration of genomic data analysis for demonstrating potential targets in the subgroup populations of squamous cell lung cancer patients
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3. Systems Biology in Biomarker Development for Cancer Signaling Therapy
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4. Comparative studies on the high-performance compression of SARS-CoV-2 genome collections
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5. Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors
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6. Multi-omics and Its Clinical Application
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7. OmicsARules: a R package for integration of multi-omics datasets via association rules mining
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8. Modulation of proliferation factors in lung adenocarcinoma with an analysis of the transcriptional consequences of genomic EGFR activation
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9. Disease subtyping using community detection from consensus networks
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10. Integration of Proteomics and Other Omics Data
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11. Hierarchical Categorical Generative Modeling for Multi-omics Cancer Subtyping
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12. Factor analysis for survival time prediction with informative censoring and diverse covariates
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13. High‐dimensional integrative copula discriminant analysis for multiomics data
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14. Integrating genomics, transcriptomics, proteomics, metabolomics, and other omics
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15. DSCC: disease subtyping using spectral clustering and community detection from consensus networks
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16. Subtype-HM: A Novel Cancer Subtype Identification Method Based on Hypergraph Learning and Multi-omics Data
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17. Multi-omics factor analysis identifies the Tensin 1-Fermitin family homologue 2–Fibronectin 1–Integrin signaling axis as a prognostic determinant in colorectal cancer
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18. Integrated multiomics analysis and advanced machine learning techniques to refine molecular subtypes, stratify prognosis, characterize tumor microenvironment, and identify distinct sensitivity patterns to frontline therapies in lung squamous cell carcinoma
    Shi Yan, Xu Zhang, Tongying Xu, Xiangyu Wang, Fanglin Tian, Weina Fan, Yuning Zhan, Cai Li, Ying Xing · 2026 · Journal Of Big Data
19. The impact of epigenetics on tumor metabolism: Friend or foe in drug response?
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20. NOODAI: a webserver for network-oriented multi-omics data analysis and integration pipeline
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21. Benchmarking multi-omics integrative clustering methods for subtype identification in colorectal cancer
    Shuai Zhang, Jiali Lv, Jinglan Zhang, Zhe Fan, Bingbing Gu, Bingbing Fan, Chunxia Li, Cheng Wang, Tao Zhang · 2025 · Computer Methods and Programs in Biomedicine
22. iModMix: integrative module analysis for multi-omics data
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23. A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure
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24. Discovery of pan-cancer related genes via integrative network analysis
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25. Multi-omics Data Deconvolution and Integration: New Methods, Insights, and Translational Implications
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26. netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity
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27. Integrating genetic and gene expression data in network-based stratification analysis of cancers
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28. Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset
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29. Patient Similarity Network of Multiple Myeloma Identifies Patient Subgroups with Distinct Genetic and Clinical Features
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30. SOJNMF: Identifying Multidimensional Molecular Regulatory Modules by Sparse Orthogonality-Regularized Joint Non-Negative Matrix Factorization Algorithm
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31. Drug repositioning via matrix completion with multi‐view side information
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32. REBET: a method to determine the number of cell clusters based on batch effect removal
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33. Splice Expression Variation Analysis (SEVA) for Inter-tumor Heterogeneity of Gene Isoform Usage in Cancer
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34. Novel cancer subtyping method based on patient-specific gene regulatory network
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35. Multi-Affinity network integration based on multi-omics data for tumor stratification
    Feng Li, Yanru Gao, Zhensheng Sun, Shengjun Li, Junliang Shang, Jin‐Xing Liu · 2025 · Biomedical Signal Processing and Control
36. Multi-omic latent variable data integration reveals multicellular structure pathways associated with resistance to tuberculin skin test (TST)/interferon gamma release assay (IGRA) conversion in Uganda
    Madison S Cox, Kimberly A. Dill‐McFarland, Jason D. Simmons, Penelope H. Benchek, Harriet Mayanja‐Kizza, W. Henry Boom, Catherine M. Stein, Thomas R. Hawn · 2025 · BMC Genomics
37. Integrating multi-omics data to optimize immunotherapy in endometrial cancer: a comprehensive study
    Xin Guan, Rongchuan Cao, Longbi Liu, Lin Ma, Ning Gao, Yanfei Yang, Mingyue Xiao, Rui Du, Yuzhe Su, Zhen Wang et al. · 2025 · Discover Oncology
38. Disease Dynamics: Insights from Microbiome and Multi-Omics Analysis
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39. SeOMLR: one-step multi-view latent representation with self-weighted ensemble learning for multi-omics cancer subtyping
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40. “Watching the Detectives” report of the general assembly of the EU project DETECTIVE Brussels, 24–25 November 2015
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41. Computational Methods in Epigenetics
    Vanessa Aguiar‐Pulido, Victoria Suárez-Ulloa, Jose Maria Eirin-Lopez, Javier Pereira, Giri Narasimhan · 2015 · Elsevier eBooks
42. Personalized Disease Phenotypes from Massive OMICs Data
    Hans Binder, Lydia Hopp, Kathrin Lembcke, Henry Loeffler‐Wirth · 2016 · IGI Global eBooks
43. Information-incorporated sparse convex clustering for disease subtyping
    Xiaoyu Zhang, Ching‐Ti Liu · 2023 · Bioinformatics
44. intCC: An efficient weighted integrative consensus clustering of multimodal data
    Can Huang, Pei Fen Kuan · 2023 · Pacific Symposium on Biocomputing (PSB)
45. The Function of the Immune System, Beyond Strategies Based on Cell‐Autonomous Mechanisms, Determines Cancer Development: Immune Response and Cancer Development
    Hamid Zand, Katayoun Pourvali · 2024 · Advanced Biology
46. Methods and tools for OMICS data integration
    Ilya S. Belalov, Xinmin Li · 2024 · Elsevier eBooks
47. Integrating machine learning with OMICs data for early detection in breast cancer
    Jiaqi Mu, Aquib Nazar, Mohd Alauddin Mohd Ali, Athar Hussain · 2025 · Gene Reports
48. SNeCT: Scalable Network Constrained Tucker Decomposition for Multi-Platform Data Profiling
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49. Clustering of Transcriptomic Data for Identification of Cancer Subtypes
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50. Testing equivalence of clustering
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51. Approaches to uncovering cancer diagnostic and prognostic molecular signatures
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52. Molecular Basis of Colorectal Cancer: Tumor Biology
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53. Similarity Network Fusion Based on Random Walk and Relative Entropy for Cancer Subtype Prediction of Multigenomic Data
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54. Integrative analysis of multiple types of genomic data using an accelerated failure time frailty model
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55. Numerical Representations of Metabolic Systems
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56. Gene Selection Using Integrative Analysis of Multi‐Level Omics Data: A Systematic Review
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57. Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection
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58. Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples
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59. A Review of Computational Learning and IoT Applications to High‐Throughput Array‐Based Sequencing and Medical Imaging Data in Drug Discovery and Other Health Care Systems
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60. iSubGen generates integrative disease subtypes by pairwise similarity assessment
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61. MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types
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62. LUCIDus: An R Package For Implementing Latent Unknown Clustering By Integrating Multi-omics Data (LUCID) With Phenotypic Traits
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63. Advanced Machine Learning and Multiomics Reveal Tumor Dynamics and Therapeutic Opportunities in Glioblastoma Multiforme
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64. Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding
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65. MuLeCoG: multi-level contrastive graph network for cancer subtype classification
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66. Recent advances in omics-based research of mitral valve disease
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67. AmalgamScope: Merging Annotations Data across the Human Genome
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68. Toxicogenomics and Toxicoinformatics: Supporting Systems Biology in the Big Data Era
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69. A unified graph model based on molecular data binning for disease subtyping
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70. Applications of hybrid data mining technique in cancer prediction and prognosis in an IoT environment
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71. 3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection
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72. Statistical and Computational Methods for Proteogenomic Data Analysis
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73. ADSVAE: An Adaptive Density-aware Spectral Clustering Method for Multi-omics Data Based on Variational Autoencoder
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74. The multiomics revolution in the era of deep learning: Allies or enemies?
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75. An extension of latent unknown clustering integrating multi-omics data (LUCID) incorporating incomplete omics data
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76. Molecular Subtypes of Non-Muscle-Invasive Bladder Cancer
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77. Multiview cluster aggregation and splitting, with an application to multiomic breast cancer data
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78. Efficient Reasoning upon Fusion of Many Data Sources
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79. Fusing heterogeneous data sets
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80. Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering
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81. Tensor-decomposition-based unsupervised feature extraction applied to prostate cancer multiomics data
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82. Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model
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83. Identification of Biomarker Genes Based on Multi-Omics Analysis in Non- Small Cell Lung Cancer
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84. Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data
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85. Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes
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86. JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data
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87. Models Predicting Effects of Missense Mutations in Oncogenesis
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88. Biomedical data integration in cancer genomics
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89. EFFECTS OF KINSHIP AND TIMING IN FAMILIAL CANCER
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90. Srrr-cluster: Using Sparse Reduced-Rank Regression to Optimize iCluster
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91. Integrative Analysis Methods for Biological Problems Using Data Reduction Approaches
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92. Epigenetic Changes and Epigenetic Targets in Head and Neck Cancer
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93. Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data
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95. Data Integration with SUMO Detects Latent Relationships Between Patients in Lower-Grade Gliomas
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96. Discovery of Cancer Subtypes Based on Stacked Autoencoder
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97. Integrative Clustering Analysis with Application in Multi-Source Gene Expression Data
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98. Cancer Precision Drug Discovery Using Big Data and Artificial Intelligence Technologies
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99. Peer Review #2 of "Machine learning analysis of TCGA cancer data (v0.1)"
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100. Integrative Epigenomics
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