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

Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.

Bioinformatics (Oxford, England) - 15 Jan 2012

Ding Jiarui, Bashashati Ali, Roth Andrew, Oloumi Arusha, Tse Kane, Zeng Thomas, Haffari Gholamreza, Hirst Martin, Marra Marco A, Condon Anne, Aparicio Samuel, Shah Sohrab P

Abstract excerpt

MOTIVATION: The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates....

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Artificial Intelligence

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Bayes Theorem

Share to Cluster Analysis

Share to Genome

Share to Software

Share to Support Vector Machine

Source

Open in PubMed Open full text

Identifiers

PMID: 22084253
DOI: 10.1093/bioinformatics/btr629
PMCID: PMC3259434

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.PMID 22084253Read ↗

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.Semantic proximity Read ↗

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.Semantic proximity Read ↗

Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panelsSemantic proximity Read ↗

TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning FilteringSemantic proximity Read ↗

Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels.Semantic proximity Read ↗

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.Semantic proximity Read ↗

Ensemble-Based Somatic Mutation Calling in Cancer Genomes.Semantic proximity Read ↗

A machine learning approach for somatic mutation discovery.Semantic proximity Read ↗

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologiesSemantic proximity Read ↗

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.Semantic proximity Read ↗

RFcaller: a machine learning approach combined with read-level features to detect somatic mutationsSemantic proximity Read ↗

Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data.Semantic proximity Read ↗

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