2017
DOI: 10.1080/15384101.2017.1361068
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Abstract: High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly see… Show more

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Cited by 44 publications
(41 citation statements)
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“…Both individual gene expression and molecular pathway activation levels can be used for tumor diagnosis and prognosis of treatment outcomes [26][27][28][29]. However, the activation level of molecular pathways provides more robust biomarker signatures compared to individual genes [30,31]. Based on pathway activation analysis and expressions of drug target genes, an approach was proposed that makes it possible to personalize the ranking of anticancer target drugs (ATDs) for an individual tumor [27,32,33].…”
Section: Introductionmentioning
confidence: 99%
“…Both individual gene expression and molecular pathway activation levels can be used for tumor diagnosis and prognosis of treatment outcomes [26][27][28][29]. However, the activation level of molecular pathways provides more robust biomarker signatures compared to individual genes [30,31]. Based on pathway activation analysis and expressions of drug target genes, an approach was proposed that makes it possible to personalize the ranking of anticancer target drugs (ATDs) for an individual tumor [27,32,33].…”
Section: Introductionmentioning
confidence: 99%
“…Oncobox utilizes an algorithm that quantitatively analyzes the extents of molecular pathway activation by calculating the value of pathway activation strength (PAS) for each molecular pathway under investigation [ 9 , 10 ]. The PAS approach has proven its advantage over the level of single gene expressions by producing less significant batch effects and platform bias, as shown for many available microarray, deep sequencing, and quantitative proteomics platforms [ 11 ]. Positive PAS value indicates upregulation of a molecular pathway compared to the control biosample or group of biosamples, negative PAS value—downregulation, and zero value suggests no changes in pathway activation.…”
Section: Resultsmentioning
confidence: 99%
“…The network based multi-omics data integration thus provides us the opportunity to incorporate information across multiple biological layers and describe the gastric cancer ( Figure 1 ). For the transcriptome, proteome, and metabolome data, network inference, pathway enrichment analysis and network module identification are three principal steps in network based integration ( Borisov et al, 2017 ; Chiappino-Pepe et al, 2017 ; Yan et al, 2017 ). Both the top-down approaches using available experimental data and the bottom-up approaches using reconstructed networks from related organisms as a scaffold to assemble new biological networks with published data are main strategies to infer biological networks ( Chiappino-Pepe et al, 2017 ).…”
Section: Data-driven Integration Approaches In Gastric Cancer Researcmentioning
confidence: 99%