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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

Groups for meta‐analysis with four PMs.
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cam4540-fig-0011: Groups for meta‐analysis with four PMs.

Mentions: In the previous analyses two PMs (absolute value of differences in means and absolute value of differences in medians) were used. In this analysis, MCO meta‐analysis is carried out using four PMs, which were the absolute value of differences in medians for each group 16. The medians were used for their nonparametric characteristics, as it has been habitual in analyses previously carried out by our group. Continuing with the case, the difference in medians between the groups of cancer and control tissues is calculated for each one of the 22,283 genes in the database. These groups are: HNS (15 samples) versus CNS (16 samples), HNS (15 samples) versus CCS (24 samples), HCS (16 samples) versus CNS (16 samples), HCS (16 samples) versus CCS (24 samples) as seen in Figure 11.


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)

Groups for meta‐analysis with four PMs.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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

cam4540-fig-0011: Groups for meta‐analysis with four PMs.
Mentions: In the previous analyses two PMs (absolute value of differences in means and absolute value of differences in medians) were used. In this analysis, MCO meta‐analysis is carried out using four PMs, which were the absolute value of differences in medians for each group 16. The medians were used for their nonparametric characteristics, as it has been habitual in analyses previously carried out by our group. Continuing with the case, the difference in medians between the groups of cancer and control tissues is calculated for each one of the 22,283 genes in the database. These groups are: HNS (15 samples) versus CNS (16 samples), HNS (15 samples) versus CCS (24 samples), HCS (16 samples) versus CNS (16 samples), HCS (16 samples) versus CCS (24 samples) as seen in Figure 11.

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