Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules

Our goal is to cluster genes into transcriptional modules - sets of genes where similarity in expression is explained by common regulatory mechanisms at the transcriptional level. We want to learn modules from both time series gene expression data and genome-wide motif data that are now readily available for organisms such as S. cereviseae as a result of prior computational studies or experimental results. We present a generative probabilistic...