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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

Problem representation where G = {gi}, i = 1,2,3,…,n and gi*∈G.
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cam4540-fig-0001: Problem representation where G = {gi}, i = 1,2,3,…,n and gi*∈G.

Mentions: Figure 1 shows the elements of the graphical representation of the MCO problem. G denotes the universe of solutions that comprises the n genes to be analyzed with gi representing each gene under analysis, (i = 1, 2, … n). Figure 2 shows the space defined by two criteria or PMs under analysis, m1 and m2. In the generalization of this figure, mik is the value for the i‐th gene in the k‐th PM. Then k = 1, 2, … C, where C is the number of PMs considered in the analysis. The Pareto‐efficient frontier in Figure 2 is formed by the genes gi*. These genes have indeed the best possible balances among the two PMs to be minimized and are the ones proposed as potential biomarkers.


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)

Problem representation where G = {gi}, i = 1,2,3,…,n and gi*∈G.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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

cam4540-fig-0001: Problem representation where G = {gi}, i = 1,2,3,…,n and gi*∈G.
Mentions: Figure 1 shows the elements of the graphical representation of the MCO problem. G denotes the universe of solutions that comprises the n genes to be analyzed with gi representing each gene under analysis, (i = 1, 2, … n). Figure 2 shows the space defined by two criteria or PMs under analysis, m1 and m2. In the generalization of this figure, mik is the value for the i‐th gene in the k‐th PM. Then k = 1, 2, … C, where C is the number of PMs considered in the analysis. The Pareto‐efficient frontier in Figure 2 is formed by the genes gi*. These genes have indeed the best possible balances among the two PMs to be minimized and are the ones proposed as potential biomarkers.

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