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 classification based on multiple dimensions: SNV patterns.

Computers in biology and medicine - 1 Dec 2022

Li Bo, Yu Liang, Gao Lin

Abstract excerpt

BACKGROUND: The occurrence of cancer is closely related to single nucleotide variants (SNVs). However, in DNA samples collected from patients with distinct cancers, SNVs are detected in different patterns. Therefore, it is an important task to select the appropriate method by which to classify cancer to the greatest extent of SNV patterns, which will aid in cancer diagnosis and treatment. In traditional studies,...

Read the complete abstract on PubMed

Topics

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

Share to Oncogenes
Share to Cluster Analysis
Share to Mutation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Neoplasms

Share to Nucleotides

Source

Identifiers

PMID: 36395594
DOI: 10.1016/j.compbiomed.2022.106270

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

Cancer classification based on multiple dimensions: SNV patterns.PMID 36395594Read ↗

pyCancerSig: subclassifying human cancer with comprehensive single nucleotide, structural and microsatellite mutational signature deconstruction from whole genome sequencingSemantic proximity Read ↗

pyCancerSig: subclassifying human cancer with comprehensive single nucleotide, structural and microsatellite mutational signature deconstruction from whole genome sequencing.Semantic proximity Read ↗

Tracking Cancer Genetic Evolution using OncoTrack.Semantic proximity Read ↗

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE).Semantic proximity Read ↗

DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types.Semantic proximity Read ↗

Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.Semantic proximity Read ↗

Mis-annotated multi nucleotide variants in public cancer genomics datasets can lead to inaccurate mutation calls with significant implicationsSemantic proximity Read ↗

Unique K-mer sequences for validating cancer-related substitution, insertion and deletion mutationsSemantic proximity Read ↗

stSNV: a comprehensive resource of SNVs in spatial transcriptome.Semantic proximity Read ↗

SpatialSNV: A novel method for identifying and analyzing spatially resolved SNVs in tumor microenvironments.Semantic proximity Read ↗

Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications.Semantic proximity Read ↗

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA.Semantic proximity Read ↗

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