scholarly journals Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19–Related Studies

2021 ◽  
pp. 195-215
Author(s):  
Anna Bernasconi ◽  
Silvia Cascianelli
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Computational analysis of microarray gene expression profiles of lung cancer

2016 ◽  
Vol 32 (1) ◽  
pp. 70-79 ◽  
Author(s):  
S. A. Babichev ◽  
A. I. Kornelyuk ◽  
V. I. Lytvynenko ◽  
V. V. Osypenko
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Computational Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Mycoplasma infection significantly alters microarray gene expression profiles

BioTechniques ◽  
2003 ◽  
Vol 35 (4) ◽  
pp. 812-814 ◽  
Author(s):  
Crispin J. Miller ◽  
Heba S. Kassem ◽  
Stuart D. Pepper ◽  
Yvonne Hey ◽  
Timothy H. Ward ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mycoplasma Infection ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Prediction of Microbial Infection of Cultured Cells Using DNA Microarray Gene-Expression Profiles of Host Responses

2012 ◽  
Vol 27 (10) ◽  
pp. 1129 ◽  
Author(s):  
Yu Rang Park ◽  
Tae Su Chung ◽  
Young Joo Lee ◽  
Yeong Wook Song ◽  
Eun Young Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Microarray ◽  
Cultured Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Host Responses ◽  
Microbial Infection ◽  
Microarray Gene Expression ◽  
Microarray Gene

Simplifying Complex Microarray Data To Derive Gene Expression Profiles Which Identify Childhood Acute Lymphoblastic Leukaemia Patients at Risk of Relapse.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 4506-4506
Author(s):  
Dachuan Guo ◽  
Alex Fong ◽  
Andy Lail ◽  
Maree O’Sullivan ◽  
Glenn Stone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukaemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression ◽  
Treatment Regimens ◽  
Childhood Acute Lymphoblastic Leukaemia ◽  
Microarray Gene ◽  
Standard Risk ◽  
Established Treatment

Abstract The optimal treatment of patients with childhood acute lymphoblastic leukaemia (ALL) depends on establishing accurate diagnosis. Our investigations seek to strategically develop the application of microarray gene expression profiling to identify ALL patients with clinically homogenous presentations but which may respond differently to established treatment regimens. We have determined the gene expression profiles of ALL bone marrow (BM) samples taken from patients at diagnosis. Data analysis has focussed on the use of a novel and innovative statistical technology, Gene-RaVE. This series of patent protected algorithms builds a multinomial regression model using Bayesian variable selection. Gene-RaVE leads to the generation of a parsimonious and simple diagnostic gene expression signature, but which provides increased predictive ability over current analysis approaches. We describe our analysis of both Affymetrix (HU133A) and cDNA (10.5K) microarray gene expression profiles generated from diagnostic BM from >100 ALL patients covering the broad ALL subtypes including T and B lineage as well as T cell lymphoma leukaemia. Comparison of gene expression data failed to identify clearly distinguishing profiles between patients identified as ‘standard risk’ from ‘medium risk’ according to BFM95 clinical criteria. Gene expression profiles from a cohort of ALL patients, identified as ‘standard risk’ at diagnosis, were compared on the basis of their overall clinical outcome: relapse within 2 yrs vs non-relapse. Using a range of analyses including t-test, Gene-RaVE, discriminant analysis approaches and principle component analysis, we have discovered that small subsets of genes (<10), all of which included Nedd4BP3 and Ribosomal Protein L38 (RPL38), can be used to distinguish the two outcome groups. Subsequent validation using real time PCR supports the increase in Nedd4BP3 expression in standard risk patients which do not respond well to established treatment regimens. The Gene-RaVE algorithm also provides a generic framework for survival analysis. This approach indicates that the expression of these Nedd4BP3, RPL38 and inositol 1, 4, 5-triphosphate receptor, type 2 can be used to build a survival ‘index’ which correlates with the time to a relapse event in standard risk childhood ALL patients. Our results are suggestive of a way forward in the development of an informative, yet efficient diagnostic tool for this childhood malignancy using microarray gene expression analysis technology.

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