Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing - 1 Jan 2015

Kim Dokyoon, Li Ruowang, Dudek Scott M, Wallace John R, Ritchie Marylyn D

Abstract excerpt

Enormous efforts of whole exome and genome sequencing from hundreds to thousands of patients have provided the landscape of somatic genomic alterations in many cancer types to distinguish between driver mutations and passenger mutations. Driver mutations show strong associations with cancer clinical outcomes such as survival. However, due to the heterogeneity of tumors, somatic mutation profiles are exceptionally...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers, Tumor
Share to Carcinoma, Renal Cell

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computational Biology

Share to Databases, Genetic

Share to Kidney Neoplasms

Share to Neural Networks, Computer

Share to Prognosis

Share to Survival Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 25592572
PMCID: PMC4299944

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma.PMID 25592572Read ↗

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes.Semantic proximity Read ↗

Cancer subtype identification using somatic mutation dataSemantic proximity Read ↗

Computational approaches to identify functional genetic variants in cancer genomes.Semantic proximity Read ↗

Cancer driver mutation prediction through Bayesian integration of multi-omic data.Semantic proximity Read ↗

Assessing the clinical utility of cancer genomic and proteomic data across tumor types.Semantic proximity Read ↗

Cancer subtype identification using somatic mutation data.Semantic proximity Read ↗

Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing.Semantic proximity Read ↗

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomesSemantic proximity Read ↗

Finding cancer driver mutations in the era of big data researchSemantic proximity Read ↗

Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients.Semantic proximity Read ↗

Systematic identification of significantly mutated regions reveals a rich landscape of functional molecular alterations across cancer genomesSemantic proximity Read ↗

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.