ArticleInternational journal of molecular sciences2023
Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.Biomarker research · 2026Review
- USP10 stabilizes FOXM1 through KAT5-mediated acetylation to suppress cell senescence and promote melanoma malignant progression.International journal of biological sciences · 2026Article
- Unleashing the potential role of tumor-associated NK cells as a novel immunotherapeutic target in triple-negative breast cancer.Breast cancer research : BCR · 2025Article
- Machine learning on transcription factor expression profiles for precision breast cancer therapy.Cancer cell international · 2025Article
- Tailored therapies for triple-negative breast cancer: current landscape and future perceptions.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Unveiling Novel miRNA-mRNA Interactions and Their Prognostic Roles in Triple-Negative Breast Cancer: Insights into miR-210, miR-183, miR-21, and miR-181b.International journal of molecular sciences · 2025Article
- Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.Biochemical genetics · 2024Article
- Plac8-ERK pathway modulation of monocyte function in sepsis.Cell death discovery · 2024Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
Transcription factors (TFs) have been shown to play a key role in the occurrence and development of tumors, including triple-negative breast cancer (TNBC), with a worse prognosis. Machine learning is widely used for establishing prediction models and screening key tumor drivers. Current studies lack TF integration in TNBC, so targeted research on TF prognostic models and targeted drugs is beneficial to improve clinical translational application. The purpose of this study was to use the Least Absolute Shrinkage and Selection Operator to build a prognostic TFs model after cohort normalization based on housekeeping gene expression levels. Potential targeted drugs were then screened on the basis of molecular docking, and a multi-drug combination strategy was used for both in vivo and in vitro experimental studies. The machine learning model of TFs built by
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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 OpenQuestion graph.