ArticlePloS one2011
Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?
Article in PloS one, 2011. 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.
- Molecular subtyping in colorectal cancer: A bridge to personalized therapy (Review).Oncology letters · 2023Review
- The Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy and the Epithelial-Mesenchymal Transition.International journal of molecular sciences · 2023Article
- Artificial intelligence in breast cancer diagnostics.Cell reports. Medicine · 2022Article
- Classification of triple negative breast cancer by epithelial mesenchymal transition and the tumor immune microenvironment.Scientific reports · 2022Article
- Deep learning in cancer diagnosis, prognosis and treatment selection.Genome medicine · 2021Review
- Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer.Frontiers in genetics · 2021Article
- Machine learning and deep learning methods that use omics data for metastasis prediction.Computational and structural biotechnology journal · 2021Review
- Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.Bioinformatics (Oxford, England) · 2020Article
- Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.Breast cancer research : BCR · 2020Article
- DeepCC: a novel deep learning-based framework for cancer molecular subtype classification.Oncogenesis · 2019Article
- Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities.Seminars in cancer biology · 2019Review
- Evaluation of variable selection methods for random forests and omics data sets.Briefings in bioinformatics · 2019Article
- Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers.Frontiers in oncology · 2019Review
- Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.Molecular oncology · 2016Article
- Machine learning applications in cancer prognosis and prediction.Computational and structural biotechnology journal · 2015Review
- Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings.BMC ophthalmology · 2014Article
- Chromosomal instability selects gene copy-number variants encoding core regulators of proliferation in ER+ breast cancer.Cancer research · 2014Article
- Functional characterization of breast cancer using pathway profiles.BMC medical genomics · 2014Article
- Systematic assessment of prognostic gene signatures for breast cancer shows distinct influence of time and ER status.BMC cancer · 2014Article
- Distance-based classifiers as potential diagnostic and prediction tools for human diseases.BMC genomics · 2014Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.
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