Titlebook: Microarray Data Analysis; Methods and Applicat Pietro Hiram Guzzi Book 2016Latest edition Springer Science+Business Media, LLC, part of Spr

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Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressiological systems. In this chapter, we present methods to address two aspects of issues related to integrated transcriptomic and proteomic analysis. First, due to the fact that proteomic datasets are often incomplete, and integrated analysis of partial proteomic data may introduce significant bias.

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Integrating Microarray Data and GRNs,being produced. Two of the most important and significant data sources come from microarray gene-expression experiments and respective databanks (e,g., Gene Expression Omnibus—GEO (.)), and from molecular pathways and Gene Regulatory Networks (GRNs) stored and curated in public (e.g., Kyoto Encyclop

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Biological Network Inference from Microarray Data, Current Solutions, and Assessments,evel. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. T

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Functional Analysis of microRNA in Multiple Myeloma,opment, proliferation, death, and differentiation of normal and cancer cells. Recently, abnormal expression of miRNAs has been reported in most of solid or hematopoietic malignancies, including multiple myeloma (MM), where miRNAs have been found deeply dysregulated and act as oncogenes or tumor supp

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Microarray Analysis in Glioblastomas,s to transcript-based microarrays and immunohistochemistry (IHC)-based tissue microarrays as being the preferred methods of choice in cancers of neurological origin. Microarray analysis may be carried out for various purposes including the following:.While investigators can sometimes use archived tu

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