An integrated approach for genome-wide gene expression analysis

Since efficient and relatively cheap methods were developed for determining biosequences, a lot of biosequence data has been generated. As the main pr oblem in molecular biology is the analysis of the data instead of the data acquisition, part of the study of computational biology is to extract all k inds of meaningful information from the sequences. Computer-assisted method s have become very important in analyzing biosequence data. However, most o f the current computer-assisted methods are limited to Ending motifs. Genes can be regulated in many ways, including combinations of regulatory elemen ts. This research is aimed at developing a new integrated system for genome -wide gene expression analysis. This research begins with a new motif-findi ng method, using a new objective function combining multiple well defined c omponents and an improved stochastic iterative sampling strategy. Combinato rial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. We then apply standard inductive learning al gorithms to generate hypotheses for different gene behaviors. A genome-wide gene expression analysis demonstrated the value of this novel integrated s ystem. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.