title: Linear modeling of mRNA expression levels during CNS development and injury.
abstract: Large-scale gene expression data sets are revolutionizing the field of functional genomics. However, few data analysis techniques fully exploit this entirely new class of data. We present a linear modeling approach that allows one to infer interactions between all the genes included in the data set. The resulting model can be used to generate interesting hypotheses to direct further experiments.
authors.affiliation: University of New Mexico, Department of Computer Science, Albuquerque 87131, USA.
authors.forename: P
authors.initials: P
authors.lastname: D'haeseleer
authors.forename: X
authors.initials: X
authors.lastname: Wen
authors.forename: S
authors.initials: S
authors.lastname: Fuhrman
authors.forename: R
authors.initials: R
authors.lastname: Somogyi
mesh_terms: D000818:Animals; D001930:Brain Injuries / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D018507:Gene Expression Regulation, Developmental; D006624:Hippocampus / Q000254:growth & development*; D008957:Models, Genetic; D012333:RNA, Messenger / Q000235:genetics*; D051381:Rats; D013116:Spinal Cord / Q000254:growth & development*; D013119:Spinal Cord Injuries / Q000235:genetics; D014158:Transcription, Genetic
chemical_list: D012333:RNA, Messenger