PMC:4573573 / 691-3311
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{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4573573","sourcedb":"PMC","sourceid":"4573573","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4573573","text":"1. Introduction\nTechnology advancement has accelerated the capability to generate large amounts of biological data. The capability to translate these data into usable knowledge has, however, grown at a much slower rate. The technologies used to generate these data are often rendered obsolete by newer ones before the data already available are fully analyzed and taken to their full potential for biological and medical advancement. Microarrays constitute a technology of this sort: one used to generate a large number of experiments, many of which will be greatly under-utilized. The analysis of microarrays, however, still holds a large potential for the discovery of genetic biomarkers for all types of cancer, as well as elicit their signaling pathways. Extracting this kind of knowledge from microarray experiments has historically been considered challenging, largely due to two main difficulties: (i) the use of incommensurable units across different experiments, and (ii) the lack of analysis techniques that converge to a consistent set of biomarkers. These two difficulties propagate uncertainty to the task of determining a reliable signaling pathway. To this end, this work proposes a two-step pipeline that involves (1) a meta-analysis strategy, based on multiple-criteria optimization, which circumvents both of the main difficulties described previously to detect highly differentially expressed genes; and (2) a method, based on integer programming to find the most correlated path among the genes from the previous step. The central hypothesis is that there is a strong signal of relative expression in microarrays that is effectively discoverable through mathematical optimization.\nIt is critical that the detection of genetic cancer biomarkers through meta-analysis can be carried out faster, more consistently, and more accurately in order to shorten the lead-time from data generation to data interpretation and knowledge application. The simultaneous meta-analysis of multiple experiments via optimization and the subsequent identification of the highest correlated genetic path described in this work offer these capabilities. Microarray data already in repositories can be readily analyzed and, prospectively, new high-throughput biological technologies could be fully utilized earlier in the fight against cancer. The gap between raw data and applicable biomedical/medical knowledge can be reduced significantly; especially when considering that historic biological data will now be able to be brought into perspective to design new experiments and focus on more precise aspects of exploration.","divisions":[{"label":"Title","span":{"begin":0,"end":15}}],"tracks":[]}