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

Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

17 May 2021

Abstract excerpt

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene...

Read the complete abstract on PubMed

Topics

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

Share to AI in cancer detection
Share to Gene expression and cancer classification
Share to Radiomics and Machine Learning in Medical Imaging

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 34079578
DOI: 10.3389/fgene.2021.628136

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Breast Cancer Case Identification Based on Deep Learning and Bioinformatics AnalysisPMID 34079578Read ↗

Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning.Semantic proximity Read ↗

Identification of Novel Hub Genes and Key Pathways Associated with Breast Cancer Through Various Classification ModelsSemantic proximity Read ↗

Deep Learning approach for breast cancer discovery Via gene correlationSemantic proximity Read ↗

Weighted gene co-expression network analysis reveals modules and hub genes associated with the development of breast cancerSemantic proximity Read ↗

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysisSemantic proximity Read ↗

Screening of breast cancer biomarkers based on bioinformatics analysisSemantic proximity Read ↗

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway NetworkSemantic proximity Read ↗

Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics MethodSemantic proximity Read ↗

Deep Learning for Predicting Breast Cancer: A Systematic Review of Progress and Future DirectionsSemantic proximity Read ↗

Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public datasetSemantic proximity Read ↗

Identification of Hub Genes Associated with Breast Cancer Using Integrated Gene Expression Data with Protein-Protein Interaction NetworkSemantic proximity Read ↗

Identification of New Key Genes in Breast Cancer by Co-expression Network AnalysisSemantic proximity Read ↗

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