title: Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge.
abstract: Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. 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. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. 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. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.
authors.forename: Katia I
authors.initials: KI
authors.lastname: Camacho-Cáceres
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.
authors.forename: Juan C
authors.initials: JC
authors.lastname: Acevedo-Díaz
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.
authors.forename: Lynn M
authors.initials: LM
authors.lastname: Pérez-Marty
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.
authors.forename: Michael
authors.initials: M
authors.lastname: Ortiz
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.
authors.forename: Juan
authors.initials: J
authors.lastname: Irizarry
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.
authors.forename: Mauricio
authors.identifier: http://orcid.org/0000-0002-2845-7147
authors.initials: M
authors.lastname: Cabrera-Ríos
authors.affiliation: Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.|Public Health Program, Ponce Health Sciences University, Ponce, Puerto Rico.
authors.forename: Clara E
authors.initials: CE
authors.lastname: Isaza
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking; D059467:Transcriptome
keywords: Biomarker; lung cancer; meta-analysis; multi-criteria optimization