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Exploiting ontologies, provides clues regarding the involvement of certain molecular processes in the cellular phenotypic manifestation. However, identifying individual molecular actors (genes, proteins, etc.) for targeted biological validation in a generic, prioritized, fashion, based in objective measures of their effects in the cellular physiology, remains a challenge. In this work, a new meta-analysis algorithm is proposed for the holistic interpretation of the information captured in -omic experiments, that is showcased in a transcriptomic, dynamic, DNA microarray dataset, which examines the effect of mastic oil treatment in Lewis lung carcinoma cells. Through the use of the Gene Ontology this algorithm relates genes to specific cellular pathways and vice versa in order to further reverse engineer the critical role of specific genes, starting from the results of various statistical enrichment analyses. The algorithm is able to discriminate candidate hub-genes, implying critical biochemical cross-talk. Moreover, performance measures of the algorithm are derived, when evaluated with respect to the differential expression gene list of the dataset.