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 detection via one-shot learning: integrating gene expression and genomic mutation analysis.

BMC bioinformatics - 6 Oct 2025

Petescia Alessia, Benevento Gerardo, Falanga Anna, Macaro Alessandro, Malandrino Delfina, Montefusco Alberto, Sorrentino Rosalinda, Zaccagnino Rocco

Abstract excerpt

BACKGROUND: Cancer is a complex disease influenced by numerous concurrent genetic factors that result in diverse tumor microenvironments (TMEs) across different cancer types. Large-scale genomic projects, such as The Cancer Genome Atlas, have underscored the need for molecular classification of cancer to enable more precise therapeutic strategies. Yet, traditional machine learning (ML) approaches currently face...

Read the complete abstract on PubMed

Topics

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

Share to Gene Expression Profiling
Share to Gene Expression Regulation, Neoplastic

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

Share to Gene Regulatory Networks

Share to Genome, Human

Share to Genomics

Share to Machine Learning

Share to Mutation

Share to Neoplasms

Source

Open in PubMed Open full text

Identifiers

PMID: 41053612
DOI: 10.1186/s12859-025-06257-3
PMCID: PMC12502228

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 10 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Cancer detection via one-shot learning: integrating gene expression and genomic mutation analysis.PMID 41053612Read ↗

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast CancerSemantic proximity Read ↗

Unveiling Pathogenesis and Therapeutic Targets of Metastatic Breast Cancer Through Sparse Modelling of Genomic LandscapeSemantic proximity Read ↗

Cancer mutational signatures representation by large-scale context embedding.Semantic proximity Read ↗

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.Semantic proximity Read ↗

Deep Learning model accurately classifies metastatic tumors from primary tumors based on mutational signaturesSemantic proximity Read ↗

PICASA: Decomposing patient heterogeneity of single-cell cancer data by cross-attention neural networksSemantic proximity Read ↗

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.Semantic proximity Read ↗

Interpreting Lung Cancer Health Disparity at Transcriptome LevelSemantic proximity Read ↗

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.Semantic proximity Read ↗

Learning Patient Similarity from Genomics for Precision OncologySemantic proximity Read ↗

CNAttention: an attention-based deep multiple-instance method for uncovering CNA signatures across cancersSemantic proximity Read ↗

Mutational signature learning with supervised negative binomial non-negative matrix factorization.Semantic proximity Read ↗

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