scholarly journals Reconstruction of Gene Regulatory Network of Colon Cancer Using Information Theoretic Approach

2013 ◽  
Author(s):  
K. Raza ◽  
R. Parveen
Keyword(s):  
Colon Cancer ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Gene Regulatory ◽  
Information Theoretic Approach

scholarly journals Correction: Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

2019 ◽  
Vol 79 (8) ◽  
pp. 2084-2084
Author(s):  
Camila M. Lopes-Ramos ◽  
Marieke L. Kuijjer ◽  
Shuji Ogino ◽  
Charles S. Fuchs ◽  
Dawn L. DeMeo ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Network Analysis ◽  
Drug Metabolism ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Cancer Drug ◽  
Gene Regulatory

scholarly journals MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yasser Abduallah ◽  
Turki Turki ◽  
Kevin Byron ◽  
Zongxuan Du ◽  
Miguel Cervantes-Cervantes ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Networks ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Experimental Results ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Gene Regulatory ◽  
Information Theoretic Approach ◽  
Time Series Microarray

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

scholarly journals Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

2018 ◽  
Vol 78 (19) ◽  
pp. 5538-5547 ◽  
Author(s):  
Camila M. Lopes-Ramos ◽  
Marieke L. Kuijjer ◽  
Shuji Ogino ◽  
Charles S. Fuchs ◽  
Dawn L. DeMeo ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Network Analysis ◽  
Drug Metabolism ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Cancer Drug ◽  
Gene Regulatory
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