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MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach
المؤلفون المشاركون
Abduallah, Yasser
Turki, Turki
Byron, Kevin
Du, Zongxuan
Cervantes-Cervantes, Miguel
Wang, Jason T. L.
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-01-22
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Abduallah, Yasser& Turki, Turki& Byron, Kevin& Du, Zongxuan& Cervantes-Cervantes, Miguel& Wang, Jason T. L.. 2017. MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach. BioMed Research International،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1138011
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Abduallah, Yasser…[et al.]. MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach. BioMed Research International No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1138011
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Abduallah, Yasser& Turki, Turki& Byron, Kevin& Du, Zongxuan& Cervantes-Cervantes, Miguel& Wang, Jason T. L.. MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1138011
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138011
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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