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

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.

Journal of bioinformatics and computational biology - 1 Dec 2021

Maruf Firda Aminy, Pratama Rian, Song Giltae

Abstract excerpt

Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exomes for tumor samples only without the paired normal samples. To include these types of data for extensive studies on the process of tumorigenesis, it is necessary to...

Read the complete abstract on PubMed

Topics

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

Share to Exome
Share to Exome Sequencing
Share to High-Throughput Nucleotide Sequencing

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Neoplasms

Share to Neural Networks, Computer

Source

Identifiers

PMID: 34895111
DOI: 10.1142/S0219720021400175

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.PMID 34895111Read ↗

Predictive Modeling of Novel Somatic Mutation Impacts on Cancer Prognosis: A Machine Learning Approach Using the COSMIC DatabaseSemantic proximity Read ↗

Deep learning discerns cancer mutation exclusivitySemantic proximity Read ↗

Deep convolutional neural networks for accurate somatic mutation detectionSemantic proximity Read ↗

A gradient-boosting approach for filtering de novo mutations in parent-offspring trios.Semantic proximity Read ↗

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing DataSemantic proximity Read ↗

Evaluating the analytical validity of mutation calling pipeline for tumor whole exome sequencingSemantic proximity Read ↗

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

Improved tumor-only variant calling and mutation burden estimation with VarNet-T.Semantic proximity Read ↗

Detection of Somatic Mutations in Exome Sequencing of Tumor-only Samples.Semantic proximity Read ↗

A benchmarking of deep neural network models for cancer subtyping using single point mutationsSemantic proximity Read ↗

Whole-exome sequencing reveals recurrent somatic mutation networks in cancer.Semantic proximity Read ↗

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

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