ArticleScientific reports2020
Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Zanidatamab in patients with early stage HER2-positive breast cancer: the NeoZanHER phase 2 single-arm open-label trial.Nature communications · 2026Trial
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Novel nested conformal prediction analysis to unravel complexity in patient subtyping.Frontiers in artificial intelligence · 2026Article
- An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes.Frontiers in bioinformatics · 2026Article
- Prediction of Immunotherapy Response in Hepatocellular Carcinoma Patients Using Pretreatment CT Images.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype.NPJ breast cancer · 2025Article
- MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer.International journal of molecular sciences · 2023Article
- Automated exploitation of deep learning for cancer patient stratification across multiple types.Bioinformatics (Oxford, England) · 2023Article
- Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective.Journal of mammary gland biology and neoplasia · 2023Review
- Multi-label transcriptional classification of colorectal cancer reflects tumor cell population heterogeneity.Genome medicine · 2023Article
- ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems.Communications biology · 2023Article
- Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.Academic radiology · 2023Review
- A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer.Cancer informatics · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Interpretable machine learning for genomics.Human genetics · 2022Article
- Mapping the genomic diaspora of gastric cancer.Nature reviews. Cancer · 2022Review
- Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning.Frontiers in genetics · 2022Article
- Identifying gene expression-based biomarkers in online learning environments.Bioinformatics advances · 2022Article
- Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC "intrinsic subtypes". We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.