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

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

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...

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Bibliographic Details
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
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