ArticleScientific reports2015
Predicting disease progression from short biomarker series using expert advice algorithm.
Article in Scientific reports, 2015. 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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Who cites it
8 citing papers in PubMed.
- Polydopamine-Based Molecular Imprinting Polymer Electrochemical Sensor for Neopterin Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response.NPJ systems biology and applications · 2022Article
- Variable Responses to Corneal Grafts: Insights from Immunology and Systems Biology.Journal of clinical medicine · 2020Review
- Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles.Scientific reports · 2019Article
- Personalizing Androgen Suppression for Prostate Cancer Using Mathematical Modeling.Scientific reports · 2018Article
- Parsimonious description for predicting high-dimensional dynamics.Scientific reports · 2015Article
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8 authors.
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Abstract
Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients. Although mathematical methods can potentially help clinicians to predict the progression of diseases, there is no method so far that estimates the patient state from very short time-series of a biomarker for making diagnosis and/or prognosis by employing the information of previous patients. Here, we propose a mathematical framework for integrating other patients' datasets to infer and predict the state of the disease in the current patient based on their short history. We extend a machine-learning framework of "prediction with expert advice" to deal with unstable dynamics. We construct this mathematical framework by combining expert advice with a mathematical model of prostate cancer. Our model predicted well the individual biomarker series of patients with prostate cancer that are used as clinical samples.
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