Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

• Main Authors: Fukushima, A. (Author), Hiroyasu, T. (Author), Hiwa, S. (Author), Sugimoto, M. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Bayes theorem; Bayes Theorem; Bayesian; Bayesian approaches; Bayesian networks; Conventional methods; Curricula; Decision making; Decision making process; Disease control; Forecasting; Gene expression; Gene expression profiles; genetics; Hepacivirus; hepatitis C; Hepatitis C; human; Humans; Multiple biomarkers; multiple sclerosis; Multiple Sclerosis; Numerical experiments; Patient treatment; Prediction; Therapy response; Time-course data; Time-course gene expression profiles; transcriptome; Transcriptome; Updated informations; Viruses
• Online Access: View Fulltext in Publisher

 Background: Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results: We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions: The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases. © 2021, The Author(s).