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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

Diagram representing six analyses between four different conditions for men samples (HNSM, HCSM, CNSM, CCSM). The edges of the graph list the genes in the associated Pareto‐efficient frontier.
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cam4540-fig-0010: Diagram representing six analyses between four different conditions for men samples (HNSM, HCSM, CNSM, CCSM). The edges of the graph list the genes in the associated Pareto‐efficient frontier.

Mentions: Figure 10 shows the results with an analysis similar to the one described before, but using only men samples. For this representation, as in previous cases, RAGE and SPP1 showed significant changes when controls (HNS or HCS) were compared to cancer.


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)

Diagram representing six analyses between four different conditions for men samples (HNSM, HCSM, CNSM, CCSM). The edges of the graph list the genes in the associated Pareto‐efficient frontier.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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

cam4540-fig-0010: Diagram representing six analyses between four different conditions for men samples (HNSM, HCSM, CNSM, CCSM). The edges of the graph list the genes in the associated Pareto‐efficient frontier.
Mentions: Figure 10 shows the results with an analysis similar to the one described before, but using only men samples. For this representation, as in previous cases, RAGE and SPP1 showed significant changes when controls (HNS or HCS) were compared to cancer.

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