ArticleFrontiers in molecular biosciences2023
A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.
Article in Frontiers in molecular biosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Heat shock protein-mediated remodeling of the bone immune microenvironment: mechanisms and precision therapeutic strategies for osteoporosis.Journal of translational medicine · 2026Review
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Combination Strategies with HSP90 Inhibitors in Cancer Therapy: Mechanisms, Challenges, and Future Perspectives.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Review
- Advances in Prostate Cancer Biomarkers and Probes.Cyborg and bionic systems (Washington, D.C.) · 2024Review
- A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases.International journal of molecular sciences · 2023Review
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.
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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.