about
Cancer pharmacogenomics, challenges in implementation, and patient-focused perspectivesUnderstanding Genotype-Phenotype Effects in Cancer via Network ApproachesComputational Cancer Biology: An Evolutionary PerspectiveVisualizing multidimensional cancer genomics dataInferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA DataIdentifying Cancer Driver Genes Using Replication-Incompetent Retroviral VectorsMutational landscape and significance across 12 major cancer typesLarge scale analysis of phenotype-pathway relationships based on GWAS resultsAn Evolutionary Approach for Identifying Driver Mutations in Colorectal CancerComputational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer ProgressionIdentifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive SetsStructural bioinformatics of the interactome.Computational solutions for omics data.Identification of ovarian cancer driver genes by using module network integration of multi-omics data.VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing dataIdentification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data.Collection, integration and analysis of cancer genomic profiles: from data to insight.Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.Simultaneous inference of cancer pathways and tumor progression from cross-sectional mutation data.Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.Seq2pathway: an R/Bioconductor package for pathway analysis of next-generation sequencing data.Pan-cancer analysis of TCGA data reveals notable signaling pathways.Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.Network-Based Analysis of eQTL Data to Prioritize Driver Mutations.MUFFINN: cancer gene discovery via network analysis of somatic mutation data.In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth dataA Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing DataIntegrated analysis of germline and somatic variants in ovarian cancer.Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.Discovery of cancer common and specific driver gene sets.Simulated annealing based algorithm for identifying mutated driver pathways in cancerDetecting independent and recurrent copy number aberrations using interval graphs.Identification of driver modules in pan-cancer via coordinating coverage and exclusivity.Toward a systematic understanding of cancers: a survey of the pan-cancer studyDOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes.Identification of collaborative driver pathways in breast cancer.Patient-derived xenografts, the cancer stem cell paradigm, and cancer pathobiology in the 21st century.Discovery of co-occurring driver pathways in cancerExpanding the computational toolbox for mining cancer genomes.Integrating genome and functional genomics data to reveal perturbed signaling pathways in ovarian cancers
P2860
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description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
De novo discovery of mutated driver pathways in cancer
@ast
De novo discovery of mutated driver pathways in cancer
@en
type
label
De novo discovery of mutated driver pathways in cancer
@ast
De novo discovery of mutated driver pathways in cancer
@en
prefLabel
De novo discovery of mutated driver pathways in cancer
@ast
De novo discovery of mutated driver pathways in cancer
@en
P2093
P2860
P356
P1433
P1476
De novo discovery of mutated driver pathways in cancer
@en
P2093
Benjamin J Raphael
Fabio Vandin
P2860
P304
P356
10.1101/GR.120477.111
P577
2011-06-07T00:00:00Z