About: MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
Creator	Cannataro, Mario Martino, Maria Arbitrio, Mariamena Caracciolo, Daniele Scionti, Francesca Settino, Marzia Tagliaferri, Pierosandro Tassone, Pierfrancesco
Source	PMC
abstract	Multiple Myeloma (MM) is the second most frequent haematological malignancy in the world although the related pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients’ survival could be important for understanding the initiation and progression of MM. In order to aid researchers in identifying new prognostic RNA biomarkers as targets for functional cell-based studies, the use of appropriate bioinformatic tools for integrative analysis is required. The main contribution of this paper is the development of a set of functionalities, extending TCGAbiolinks package, for downloading and analysing Multiple Myeloma Research Foundation (MMRF) CoMMpass study data available at the NCI’s Genomic Data Commons (GDC) Data Portal. In this context, we present further a workflow based on the use of this new functionalities that allows to i) download data; ii) perform and plot the Array Array Intensity correlation matrix; ii) correlate gene expression and Survival Analysis to obtain a Kaplan–Meier survival plot.
has issue date	2020-05-22(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50420-5_42
has license	no-cc
sha1sum (hex)	8f3b649330b8e9ca2529e6763e61831ab807535e
schema:url	https://doi.org/10.1007/978-3-030-50420-5_42
resource representing a document's title	MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
has PubMed Central identifier	PMC7304035
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:8f3b649330b8e9ca2529e6763e61831ab807535e#body_text
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