Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge

Camacho-Cáceres, Katia I.; Acevedo‐Díaz, Juan C.; Pérez‐Marty, Lynn M.; Ortiz, Michael; Irizarry, Juan; Cabrera-Ríos, Mauricio; Isaza, Clara E.

doi:10.1002/cam4.540

Cited by 9 publications

(8 citation statements)

References 69 publications

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“…The procedure to select the genes of interest through MCO is explained in detail Camacho-Caceres et al [ 5 ]. In brief, MCO will select a family of solutions (genes in this case) that have the best compromises between the performances measures considered in the analysis.…”

Section: Methodsmentioning

confidence: 99%

“…1 . The reader interested in MCO is referred to [ 5 ] for a detailed and illustrated explanation of the procedure.…”

Section: Methodsmentioning

confidence: 99%

“…High throughput biological experiments, like microarrays, have been used to detect potential AD biomarkers maintaining the issue of selecting genes for normalization and parameters for the analysis as reviewed in Cooper-Knock et al [ 6 ]. The optimization-driven method presented here differs from many of the commonly used informatics-based and statistics-based analysis methods in that it does not require any parameter adjustment by the user nor any normalization procedure, as described in Camacho-Caceres et al and Lorenzo et al [ 5, 7 ].…”

Section: Introductionmentioning

confidence: 99%

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A Selection of Important Genes and Their Correlated Behavior in Alzheimer’s Disease

Cruz-Rivera

Pérez‐Morales

Santiago

et al. 2018

JAD

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In 2017, approximately 5 million Americans were living with Alzheimer’s disease (AD), and it is estimated that by 2050 this number could increase to 16 million. In this study, we apply mathematical optimization to approach microarray analysis to detect differentially expressed genes and determine the most correlated structure among their expression changes. The analysis of GSE4757 microarray dataset, which compares expression between AD neurons without neurofibrillary tangles (controls) and with neurofibrillary tangles (cases), was casted as a multiple criteria optimization (MCO) problem. Through the analysis it was possible to determine a series of Pareto efficient frontiers to find the most differentially expressed genes, which are here proposed as potential AD biomarkers. The Traveling Sales Problem (TSP) model was used to find the cyclical path of maximal correlation between the expression changes among the genes deemed important from the previous stage. This leads to a structure capable of guiding biological exploration with enhanced precision and repeatability. Ten genes were selected (FTL, GFAP, HNRNPA3, COX1, ND2, ND3, ND4, NUCKS1, RPL41, and RPS10) and their most correlated cyclic structure was found in our analyses. The biological functions of their products were found to be linked to inflammation and neurodegenerative diseases and some of them had not been reported for AD before. The TSP path connects genes coding for mitochondrial electron transfer proteins. Some of these proteins are closely related to other electron transport proteins already reported as important for AD.

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Section: Methodsmentioning

confidence: 99%

“…1 . The reader interested in MCO is referred to [ 5 ] for a detailed and illustrated explanation of the procedure.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Selection of Important Genes and Their Correlated Behavior in Alzheimer’s Disease

Cruz-Rivera

Pérez‐Morales

Santiago

et al. 2018

JAD

Self Cite

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“…Several studies have shown the benefits of meta-analysis in terms of both higher statistical power and precision in detecting differentially expressed genes (DEGs) in different complex traits including infectious disease (Song et al, 2014; Camacho-Cáceres et al, 2015; Sharma et al, 2015; Yin et al, 2015; Wang C.-Y. et al, 2015; Wang X. et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways

et al. 2016

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The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein–protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.

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“…The natural variables and the simulated values of the PMs are coded using a linear transformation to make them fall in the range of [-1,1] to avoid dimensionality problems. With these coded values the efficient frontier was found, using a MATLAB code available in our group to carry out the full pairwise comparison (Camacho-Caceres et al 2015). The found efficient frontier represents the initial incumbent solution (10) as shown in Figure 4.…”

mentioning

confidence: 99%

A Simulation-Optimization strategy to deal simultaneously with tens of decision variables and multiple performance measures in manufacturing

Niño-Pérez¹,

Mendez-Vazquez²,

Cabrera-Ríos³

2015

2015 Winter Simulation Conference (WSC)

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This work addresses the multiple criteria simulation optimization problem. Such a problem entails using an optimization strategy to manipulate the parameters of a simulation model to arrive at the best possible configurations in the presence of several performance measures in conflict. Pareto Efficiency conditions are used in an iterative framework based on experimental design and pairwise comparison. In particular, this work improves upon and replaces the use of Data Envelopment Analysis to determine the efficient frontier and replaces the use of a single-pass algorithm previously proposed by our research group. The results show a rapid convergence to a more precise characterization of the Pareto-efficient solutions. In addition, the capability of the method to deal with fifty decision variables simultaneously is demonstrated through a case study in the fine-tuning of a manufacturing line. 1 978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge

Cited by 9 publications

References 69 publications

A Selection of Important Genes and Their Correlated Behavior in Alzheimer’s Disease

A Selection of Important Genes and Their Correlated Behavior in Alzheimer’s Disease

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways

A Simulation-Optimization strategy to deal simultaneously with tens of decision variables and multiple performance measures in manufacturing

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