scholarly journals Mouse Models of Prostate Cancer

2011 ◽  
Vol 2011 ◽  
pp. 1-22 ◽  
Author(s):  
Kenneth C. Valkenburg ◽  
Bart O. Williams
Keyword(s):  
Prostate Cancer ◽  
Mouse Models ◽  
Human Disease ◽  
High Throughput Screening ◽  
Genetically Engineered ◽  
Screening Methods ◽  
Human Prostate Cancer ◽  
Genetic Changes ◽  
Genetic Pathways ◽  
Genetically Engineered Mouse Models

The development and optimization of high-throughput screening methods has identified a multitude of genetic changes associated with human disease. The use of immunodeficient and genetically engineered mouse models that mimic the human disease has been crucial in validating the importance of these genetic pathways in prostate cancer. These models provide a platform for finding novel therapies to treat human patients afflicted with prostate cancer as well as those who have debilitating bone metastases. In this paper, we focus on the historical development and phenotypic descriptions of mouse models used to study prostate cancer. We also comment on how closely each model recapitulates human prostate cancer.

scholarly journals TarGo: network based target gene selection system for human disease related mouse models

2019 ◽  
Vol 35 (1) ◽  
Author(s):  
Daejin Hyung ◽  
Ann-Marie Mallon ◽  
Dong Soo Kyung ◽  
Soo Young Cho ◽  
Je Kyung Seong
Keyword(s):  
Mouse Models ◽  
Network Topology ◽  
Human Disease ◽  
Target Gene ◽  
Gene Selection ◽  
Genetically Engineered ◽  
Human Diseases ◽  
Selection System ◽  
Genetically Engineered Mouse Models ◽  
Disease Signatures

Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.

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