Highly parallel technologies and customizable experiments are becoming important at a time when personal genome project, pharmogenomics, and proteomics are burgeoning research areas.  Traditional approaches have drawn from expert knowledge to arbitrarily select genes/proteins to be analyzed while taking into account the multiplexed equipment’s size constraints and the investigator’s desired molecular research area of focus. 

Using an information theoretic framework, the Gene Ontology Information Cube (GO Infocube) allows investigators to optimally design experiments that take advantage of parallelizable technologies (e.g. from custom spotted oligonucleotide and protein arrays to gel electrophoresis).  Investigators can decide on which molecular research areas they want to focus on- and how much they wish to invest (in terms of number of genes/proteins to explore).  The GO Infocube then constrains the search for genes/proteins based on these criteria and optimally selects those which would optimize the potential information gained once the experiment is performed.  The GO Infocube allows ones to visualize information/specificity of sets of genes/proteins across three GO dimensions of interest.  This allows investigators to compare existing multiplexed experimental setups to see the information content of their current designs.