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

Camacho-Cáceres KI, Acevedo-Díaz JC, Pérez-Marty LM, Ortiz M, Irizarry J, Cabrera-Ríos M, Isaza CE - Cancer Med (2015)

Bottom Line: These data, however, are stored and often times abandoned when new experimental technologies arrive.This work reexamines lung cancer microarray data with a novel multiple criteria optimization-based strategy aiming to detect highly differentially expressed genes.In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer.

View Article: PubMed Central - PubMed

Affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.

No MeSH data available.


Related in: MedlinePlus

Graphical and Mathematical representation of the sample problem. (A) The six candidate solutions of the sample problem. (B) Mathematical formulation of the problem.
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cam4540-fig-0003: Graphical and Mathematical representation of the sample problem. (A) The six candidate solutions of the sample problem. (B) Mathematical formulation of the problem.

Mentions: Let G = {g1, g2, g3, g4, g5, g6} be a set of n = 6 genes. The values for the PMs per gene are g1(1, 4); g2(3,4); g3(5,6); g4(7,5); g5(3,2); g6(4,1). This leads to having {m11m21m31m41m51m61} = {1, 3, 5, 7, 3, 4} and {m12m22m32m42m52m62} = {4, 4, 6, 5, 2, 1}. Figure 3 shows the MCO problem for the case of minimization of both performance measures and its mathematical solution.


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

Camacho-Cáceres KI, Acevedo-Díaz JC, Pérez-Marty LM, Ortiz M, Irizarry J, Cabrera-Ríos M, Isaza CE - Cancer Med (2015)

Graphical and Mathematical representation of the sample problem. (A) The six candidate solutions of the sample problem. (B) Mathematical formulation of the problem.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940807&req=5

cam4540-fig-0003: Graphical and Mathematical representation of the sample problem. (A) The six candidate solutions of the sample problem. (B) Mathematical formulation of the problem.
Mentions: Let G = {g1, g2, g3, g4, g5, g6} be a set of n = 6 genes. The values for the PMs per gene are g1(1, 4); g2(3,4); g3(5,6); g4(7,5); g5(3,2); g6(4,1). This leads to having {m11m21m31m41m51m61} = {1, 3, 5, 7, 3, 4} and {m12m22m32m42m52m62} = {4, 4, 6, 5, 2, 1}. Figure 3 shows the MCO problem for the case of minimization of both performance measures and its mathematical solution.

Bottom Line: These data, however, are stored and often times abandoned when new experimental technologies arrive.This work reexamines lung cancer microarray data with a novel multiple criteria optimization-based strategy aiming to detect highly differentially expressed genes.In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer.

View Article: PubMed Central - PubMed

Affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.

No MeSH data available.


Related in: MedlinePlus