U sing Sym m etric Causal Independence M odels to P red ict G ene Expression from Sequence D ata

We present an approach for inferring transcriptional regulatory modules from genome sequence and gene expression data. Our method, which is based on symmetric causal independence models, is both able to model the logic behind transcriptional regulation and to incorporate uncertainty about the functionality of putative transcription factor binding sites. Applying our approach to the deadliest species of human malaria parasite, Plasmodium falciparum, we obtain several striking results that deserve further (biological) investigation.