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

Estimating cancer survival and clinical outcome based on genetic tumor progression scores.

Bioinformatics (Oxford, England) - 15 May 2005

Rahnenführer Jörg, Beerenwinkel Niko, Schulz Wolfgang A, Hartmann Christian, von Deimling Andreas, Wullich Bernd, Lengauer Thomas

Abstract excerpt

MOTIVATION: In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. Survival prognosis is typically based on clinical and histological parameters. There is increasing interest in identifying genetic markers that better capture the status of a tumor in order to improve on existing predictions. The accumulation of genetic...

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 Chromosome Aberrations

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Diagnosis, Computer-Assisted

Share to Disease Progression

Share to Proportional Hazards Models

Share to Risk Assessment

Share to Survival Analysis

Share to Survival Rate

Source

Identifiers

PMID: 15705654
DOI: 10.1093/bioinformatics/bti312

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

Estimating cancer survival and clinical outcome based on genetic tumor progression scores.PMID 15705654Read ↗

Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores.Semantic proximity Read ↗

Application of oncogenetic trees mixtures as a biostatistical model of the clonal cytogenetic evolution of meningiomasSemantic proximity Read ↗

Inferring Tumor Progression in Large DatasetsSemantic proximity Read ↗

<tt>TRONCO</tt> : an R package for the inference of cancer progression models from heterogeneous genomic dataSemantic proximity Read ↗

pathTiMEx: Joint Inference of Mutually Exclusive Cancer Pathways and Their Progression DynamicsSemantic proximity Read ↗

Inferring tumor progression in large datasets.Semantic proximity Read ↗

Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model.Semantic proximity Read ↗

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic dataSemantic proximity Read ↗

Brain tumour genetic network signatures of survival.Semantic proximity Read ↗

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?Semantic proximity Read ↗

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?Semantic proximity Read ↗

Mtreemix: a software package for learning and using mixture models of mutagenetic trees.Semantic proximity Read ↗

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