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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

Figures represent the results of genes with high DE using Volcano plot and varying the P‐values and FC. The dark blue color indicates the genes most differentially expressed.
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cam4540-fig-0012: Figures represent the results of genes with high DE using Volcano plot and varying the P‐values and FC. The dark blue color indicates the genes most differentially expressed.

Mentions: The original database GDS3257 of lung cancer was used for this analysis. The samples HNS and CNS were used to build the Volcano plot. As mentioned previously, the Volcano plot requires the user to define thresholds for two parameters: p‐value and FC to select genes. A 32 factorial experiment was used to explore these parameters as shown in Figure A1. The results are shown in Table A1.


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)

Figures represent the results of genes with high DE using Volcano plot and varying the P‐values and FC. The dark blue color indicates the genes most differentially expressed.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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

cam4540-fig-0012: Figures represent the results of genes with high DE using Volcano plot and varying the P‐values and FC. The dark blue color indicates the genes most differentially expressed.
Mentions: The original database GDS3257 of lung cancer was used for this analysis. The samples HNS and CNS were used to build the Volcano plot. As mentioned previously, the Volcano plot requires the user to define thresholds for two parameters: p‐value and FC to select genes. A 32 factorial experiment was used to explore these parameters as shown in Figure A1. The results are shown in Table A1.

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