ArticleBriefings in bioinformatics2024
AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
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
9 citing papers in PubMed.
- Construction of EGFR-Targeted Triptolide Liposomes Using Uniform Design Optimization and Therapeutic Evaluation in Gliomas.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Addressing the challenges of immunogenicity, heterogeneity and tumorigenicity in the clinical translation of human embryonic stem cells.Stem cell research & therapy · 2025Review
- Single-cell multi-omics and machine learning for dissecting stemness in cancer.Briefings in bioinformatics · 2025Review
- Protein Marker-Dependent Drug Discovery Targeting Breast Cancer Stem Cells.International journal of molecular sciences · 2025Review
- Cancer stem cells: landscape, challenges and emerging therapeutic innovations.Signal transduction and targeted therapy · 2025Review
- Enhancing the clinical translation of stem cell models by focusing on standardization and international regulatory cooperation.World journal of stem cells · 2025Article
- Discovering the Potential Role of the C2 DUSP2+ MCs Subgroup in Lung Adenocarcinoma.Translational oncology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.
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Identifiers
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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.