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

Cancer progression models and fitness landscapes: a many-to-many relationship.

Bioinformatics (Oxford, England) - 1 Mar 2018

Diaz-Uriarte Ramon

Abstract excerpt

Motivation: The identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to identify these constraints, and return Directed Acyclic Graphs (DAGs) of restrictions where arrows indicate dependencies or...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Disease Progression
Share to Epistasis, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to CPM gene

Share to Models, Genetic

Share to Mutation

Share to Neoplasms

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 29048486
DOI: 10.1093/bioinformatics/btx663
PMCID: PMC6031050

Genes

CPM

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Cancer progression models and fitness landscapes: a many-to-many relationship.PMID 29048486Read ↗

Every which way? On predicting tumor evolution using cancer progression modelsSemantic proximity Read ↗

PMCE: efficient inference of expressive models of cancer evolution with high prognostic powerSemantic 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 ↗

A general calculus of fitness landscapes finds genes under selection in cancers.Semantic proximity Read ↗

Model genotype-phenotype mappings and the algorithmic structure of evolution.Semantic proximity Read ↗

CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional DataSemantic proximity Read ↗

Bayesian inference of fitness landscapes via tree-structured branching processesSemantic proximity Read ↗

CAPRI: efficient inference of cancer progression models from cross-sectional data.Semantic proximity Read ↗

What can we learn from fitness landscapes?Semantic proximity Read ↗

Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations.Semantic proximity Read ↗

EvAM-Tools: tools for evolutionary accumulation and cancer progression models.Semantic proximity Read ↗

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