ArticleCancer cell international2025
Machine learning on transcription factor expression profiles for precision breast cancer therapy.
Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
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Authors and funding
10 authors.
Funding
Abstract
backgroundAlthough breast cancer is a significant heterogeneous disease with an increasing global prevalence, precise prognostic evaluation is a vital aspect of designing personalized therapy strategies and upholding patients' survival rates. With the incorporation of artificial intelligence technology, in particular, machine learning, cancer prognosis and prediction have significantly been redefined.
methodsIn this study, we adopted a ten-fold cross-validation method to construct a Machine Learning-Derived Transcription Factor Signature (MDTS) across 108 algorithmic combinations. The optimal model was selected based on the highest average C-index across ten cohorts. We integrated single-cell data with multi-omics analysis to comprehensively assess the robustness of the MDTS model at both molecular and genomic levels. The MDTS demonstrated superior predictive power, outperforming 103 existing signatures and accurately predicting breast cancer outcomes across 10 independent cohorts.
resultsOur findings revealed that patients with low MDTS scores are more likely to benefit from immunotherapy, while the PAC-1 drug was identified as the most targeted agents to the chemotherapy with high MDTS score.
conclusionsThese insights will open the door to delivering cutting-edge MDTS strategies to customizing breast cancer therapies.
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Registered trials
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 Socratic graph.