ArticleNPJ systems biology and applications2021
Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Sorafenib nanomedicine in HCC: nano-bio interactions and combination therapies.Journal of nanobiotechnology · 2026Review
- Current status and future perspectives of multi-modal bacteria-based cancer therapies.Clinical and translational medicine · 2025Review
- Graph Representation Learning for the Prediction of Medication Usage in the UK Biobank Based on Pharmacogenetic Variants.Bioengineering (Basel, Switzerland) · 2025Article
- Predicting drug outcome of population via clinical knowledge graph.medRxiv : the preprint server for health sciences · 2024Article
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.Briefings in bioinformatics · 2024Article
- Review
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
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Authors and funding
6 authors.
Funding
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Abstract
The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs' mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs' effect on a given patient.
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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.