Retrieval of Experiments by Efficient Estimation of Marginal Likelihood

An experiment is an organized procedure for validating a hypothesis, and usually comprises measurements over a set of variables that are either varied (covariates or independent variables) or studied (outcomes or dependent variables). For example, in the study of genome-wide association, one explores the association between'traits' (controlled variable) and common genetic variations (response variables) [1], or in the study of functional genomics covariates can be the species, disease state, and cell type, whereas outcome can be microarray measurements [2]. Traditionally, similar experiments have been retrieved from qualitative assessment of related scientific documents without explicitly handling the experimental data. Recent technological advances have allowed researchers to both acquire measurements in an unprecedented scale throughout the globe, and to release these measurements for public use after curation, e.g., [3]. However, exploring similar experiments still relies on comparing the manual annotations which suffer extensively from variations in terminology, and incompleteness in annotations, e.g., [4]. The global effort of availing researchers with wealth of data invites the need for sophisticated retrieval systems that look beyond annotations in comparing related experiments to improve accessibility. The next step toward this goal is to compare the knowledge acquired from experimental measurements rather than just annotations.