scholarly journals Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions

2001 ◽  
Vol 314 (5) ◽  
pp. 1053-1066 ◽  
Author(s):  
Jiang Qian ◽  
Marisa Dolled-Filhart ◽  
Jimmy Lin ◽  
Haiyuan Yu ◽  
Mark Gerstein
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biologically Relevant ◽  
Local Clustering

scholarly journals Identification of Monotonically Differentially Expressed Genes for IFN-β-Treated Multiple Sclerosis Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Suyan Tian ◽  
Lei Zhang
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biologically Relevant ◽  
Therapeutic Mechanism ◽  
The Central Nervous System ◽  
Multiple Sclerosis Patients ◽  
Term Response

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

scholarly journals Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease

2019 ◽  
Author(s):  
Galen E.B. Wright ◽  
Nicholas S. Caron ◽  
Bernard Ng ◽  
Lorenzo Casal ◽  
Xiaohong Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Huntington Disease ◽  
Neurodegenerative Disorder ◽  
Age Of Onset ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cag Repeat ◽  
Biologically Relevant ◽  
Clinical Onset ◽  
Genome Wide Data

ABSTRACTHuntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in the HTT gene. In an attempt to identify genomic modifiers that contribute towards the age of onset of HD, we performed a transcriptome wide association study assessing heritable differences in genetically determined expression in diverse tissues, employing genome wide data from over 4,000 patients. This identified genes that showed evidence for colocalization and replication, with downstream functional validation being performed in isogenic HD stem cells and patient brains. Enrichment analyses detected associations with various biologically-relevant gene sets and striatal coexpression modules that are mediated by CAG length. Further, cortical coexpression modules that are relevant for HD onset were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder.

scholarly journals ShapeCluster: Applying parametric regression to analyse time-series gene expression data

2016 ◽  
Author(s):  
Philip J Law ◽  
Vicky Buchanan-Wollaston ◽  
Andrew Mead
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Parametric Models ◽  
Linear Functions ◽  
Biologically Relevant ◽  
Research Areas ◽  
Biological Stress

High-throughput technologies have made it possible to perform genome-scale analyses to investigate a variety of research areas. From these analyses, vast amounts of data are generated. However, these data can be noisy, which could obscure the underlying signal. Here, a high-throughput regression analysis approach was developed, where a variety of linear and nonlinear parametric models were fitted to gene expression profiles from time course experiments. These models include the logistic, Gompertz, exponential, critical exponential, linear+exponential, Gaussian and linear functions. The fitted parameters from these models reflect aspects of the model shape, and thus allowed for the interpretation of gene expression profiles in terms of the underlying biology, such as the time of initial gene expression. This provides a potentially more mechanistic ap-proach to studying the genetic responses to stimuli. Together with a cluster analysis, termed ShapeCluster, it was possible to group genes based on these aspects of the expression profiles. By investigating different combinations of parameters, this added flexibility to the analysis and allowed for the investigation of the data in multiple ways, including the identification of groups of genes that may be co-regulated, or participate in response to the biological stress in question. Clusters from these methods were assessed for significance through the use of over-represented annotation terms and motifs, and found to pro-duce biologically relevant sets of genes. The ShapeCluster package is available from https://sourceforge.net/projects/shapecluster/.

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
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