Arika Fukushima1, Masahiro Sugimoto2,3, Satoru Hiwa4, Tomoyuki Hiroyasu5. 1. Graduate School of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan. 2. Research and Development Center for Minimally Invasive Therapies, Institute of Medical Science, Tokyo Medical University, Shinjuku, Tokyo, 160-8402, Japan. 3. Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan. 4. Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan. 5. Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan. tomo@is.doshisha.ac.jp.
Abstract
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.
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.
Entities:
Keywords:
Bayesian; Gene expression profiles; Prediction; Therapy response; Time-course data
Authors: Andrew F Leuchter; Ian A Cook; Steven P Hamilton; Katherine L Narr; Arthur Toga; Aimee M Hunter; Kym Faull; Julian Whitelegge; Anne M Andrews; Joseph Loo; Baldwin Way; Stanley F Nelson; Steven Horvath; Barry D Lebowitz Journal: Curr Psychiatry Rep Date: 2010-12 Impact factor: 5.285