Identifying Cancer Patient Subgroups by Finding Co-Modules From the Driver Mutation Profiles and Downstream Gene Expression Profiles
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- Type
- article
- Published
- 2021-08-20
- Cited by
- 8
- References
- 70
- OpenAlex
- https://openalex.org/W3194628804
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:237255127
Keywords
Downstream (manufacturing), Cancer, Gene, Computational biology, Consistency (knowledge bases)
References
- Survival probabilities (the Kaplan-Meier method)
- Some Fundamental Concepts of Information Retrieval.
- Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
- Integrative Analyses of Cancer Data: A Review from a Statistical Perspective
- Network-assisted approaches for human disease research
- Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes
- Integrative clustering methods for high-dimensional molecular data
- Multi-platform analysis of 12 cancer types reveals molecular classification within and across tissues-of-origin
- Similarity network fusion for aggregating data types on a genomic scale
- Tumour heterogeneity in the clinic
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- TGF‐Beta Signaling in Breast Cancer
- Pattern discovery and cancer gene identification in integrated cancer genomic data
- Associating Genes and Protein Complexes with Disease via Network Propagation
- Genetic variants in the inositol phosphate metabolism pathway and risk of different types of cancer
- The causes and consequences of genetic heterogeneity in cancer evolution
- Fanconi Anemia Repair Pathway Dysfunction, a Potential Therapeutic Target in Lung Cancer
- Cancer Genome Landscapes
- A Cluster Separation Measure
- A human functional protein interaction network and its application to cancer data analysis
Cited by
- Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network
- Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm
- The multifaceted roles of COL4A4 in lung adenocarcinoma: An integrated bioinformatics and experimental study
- Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration
- Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features
- MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning.
- Design of an integrated model using U-Net, DeepSurv, and cross-attention for lung cancer classification and survival prediction
- DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and global driver genes
- Integrative Identification of Driver Genes for Enhanced Personalized Cancer Subtype Classification
- Identification and Segregation of Genes with Improved Recurrent Neural Network Trained with Optimal Gene Level and Mutation Level Features
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