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Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies.
Bernasconi, Anna; Cascianelli, Silvia.
  • Bernasconi A; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy. anna.bernasconi@polimi.it.
  • Cascianelli S; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
Methods Mol Biol ; 2401: 195-215, 2022.
Article in English | MEDLINE | ID: covidwho-1568331
ABSTRACT
The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Gene Expression Profiling / COVID-19 Limits: Humans Language: English Journal: Methods Mol Biol Journal subject: Molecular Biology Year: 2022 Document Type: Article Affiliation country: 978-1-0716-1839-4_13

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Gene Expression Profiling / COVID-19 Limits: Humans Language: English Journal: Methods Mol Biol Journal subject: Molecular Biology Year: 2022 Document Type: Article Affiliation country: 978-1-0716-1839-4_13