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

Does Relaxing the Infinite Sites Assumption Give Better Tumor Phylogenies? An ILP-Based Comparative Approach

17 Aug 2018

Abstract excerpt

Most of the evolutionary history reconstruction approaches are based on the infinite sites assumption, which states that mutations appear once in the evolutionary history. The Perfect Phylogeny model is the result of the infinite sites assumption and has been widely used to infer cancer evolution. Nonetheless, recent results show that recurrent and back mutations are present in the evolutionary history of tumors,...

Read the complete abstract on PubMed

Topics

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

Share to Cancer Genomics and Diagnostics
Share to Genetic factors in colorectal cancer
Share to Genomics and Phylogenetic Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 31603766
DOI: 10.1109/tcbb.2018.2865729

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Does Relaxing the Infinite Sites Assumption Give Better Tumor Phylogenies? An ILP-Based Comparative ApproachPMID 31603766Read ↗

Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumorsSemantic proximity Read ↗

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.Semantic proximity Read ↗

gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data.Semantic proximity Read ↗

FiMO: Inferring the Temporal Order of Mutations on Clonal Phylogeny under Finite-sites ModelsSemantic proximity Read ↗

Orchard: Building large cancer phylogenies using stochastic combinatorial search.Semantic proximity Read ↗

SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and errorSemantic proximity Read ↗

SiFit: inferring tumor trees from single-cell sequencing data under finite-sites modelsSemantic proximity Read ↗

Inferring the Mutational History of a Tumor Using Multi-state Perfect Phylogeny Mixtures.Semantic proximity Read ↗

POTTR: Identifying Recurrent Trajectories in Evolutionary and Developmental Processes using PosetsSemantic proximity Read ↗

Estimating an oncogenetic tree when false negatives and positives are presentSemantic proximity Read ↗

Inferring Tree Models for Oncogenesis from Comparative Genome Hybridization DataSemantic proximity Read ↗

Parameter, noise, and tree topology effects in tumor phylogeny inference.Semantic proximity Read ↗

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