title: Classification of patients from time-course gene expression.
abstract: Classifying patients into different risk groups based on their genomic measurements can help clinicians design appropriate clinical treatment plans. To produce such a classification, gene expression data were collected on a cohort of burn patients, who were monitored across multiple time points. This led us to develop a new classification method using time-course gene expressions. Our results showed that making good use of time-course information of gene expression improved the performance of classification compared with using gene expression from individual time points only. Our method is implemented into an R-package: time-course prediction analysis using microarray.
authors.affiliation: Stanford Genome Technology Center, Palo Alto, CA 94306, USA. yupingz@stanford.edu
authors.forename: Yuping
authors.initials: Y
authors.lastname: Zhang
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.forename: Ronald
authors.initials: R
authors.lastname: Davis
mesh_terms: D002056:Burns / Q000097:blood / Q000235:genetics*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016899:Interferon-beta / Q000627:therapeutic use; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000188:drug therapy / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011237:Predictive Value of Tests
chemical_list: D016899:Interferon-beta