Identification of potential biomarkers from microarray experiments using multiple criteria optimization

Abstract

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

“…Furthermore, the best tradeoffs among the competing criteria were identified with the application of Pareto-optimality conditions, as advocated in prior research [14,15,42]. is method is exact (as opposed to a heuristic approach [18]) and has being utilized Advances in Civil Engineeringpreviously to solve engineering and science problems [14,15].…”

Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge

“…This research adopts multiple criteria optimization and Pareto conditions to find biomarkers following the direction of our research group , and proposes extending the application to this end through simultaneous analysis of multiple independent experiments, that is carrying out meta‐analysis. In 2010, in our group, Sanchez‐Peña used a combination of two performance measures (two P ‐values) obtained from a single‐microarray database to cast the MCO problem and Data Enveloped Analysis (DEA) to solve it. The pairwise comparison scheme in the present work yields a more precise Pareto‐efficient frontier than DEA, as it can deal with nonconvexity from the onset.…”

“…As noted earlier, the MCO problem has been approached in our group by Sánchez‐Peña, et al. through Data Envelopment Analysis (DEA). This work approaches the larger problem of analyzing multiple microarray databases simultaneously that is, to carry out meta‐analysis, formulating the analysis as an MCO problem and solving it through a pairwise‐comparison scheme that facilitates the evaluation of Pareto‐efficiency conditions.…”

“…The method used to solve the associated MCO problem is a full pairwise comparison scheme that effectively finds the genes that show high expression change across multiple PMs. This scheme is an improvement in terms of precision and convergence over the Data Envelopment Analysis approach presented by our group in . The genes identified this way are located on the Pareto‐efficient frontier of the MCO problem, that is, they are demonstrably Pareto‐efficient and are, in consequence, proposed as potential biomarkers.…”

“…For the analysis, the MCO solutions will be those genes that have associated the greatest differences in the selected metrics. The solution genes are the ones that changed their expression the most between the compared conditions and could be potential biomarkers and can, after further study and confirmation, help with the diagnosis, prognosis, treatment, and recurrence prediction for the condition under analysis . It can be appreciated that the method seeks to minimize the likelihood of false positives due to its focus on frontier analysis.…”