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MR-microT: a MapReduce-based MicroRNA target prediction method

Published: 30 June 2014 Publication History

Abstract

MicroRNAs (miRNAs) are small RNA molecules that inhibit the expression of particular genes, a function that makes them useful towards the treatment of many diseases. Computational methods that predict which genes are targeted by particular miRNA molecules are known as target prediction methods. In this paper, we present a MapReduce-based system, termed MR-microT, for one of the most popular and accurate, but computational intensive, prediction methods. MR-microT offers the highly requested by life scientists feature of predicting the targets of ad-hoc miRNA molecules in near-real time through an intuitive Web interface.

References

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Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51:107--113, 2008.
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Maria D. Paraskevopoulou, Georgios Georgakilas, Ioannis S. Vlachos Nikos Kostoulas, Thanasis Vergoulis, Martin Reczko, Theodore Dalamagas Christos Filippidis, and A. G. Hatzigeorgiou. Diana-microt web server v5.0: service integration into mirna functional analysis workflows. Nucleic Acids Research, 41:W169--W173, 2013.
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Martin Reczko, Manolis Maragkakis, Panagiotis Alexiou, Ivo Grosse, and Artemis G. Hatzigeorgiou. Functional microrna targets in protein coding sequences. Bioinformatics, 28:771--776, 2012.
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  • (2021) sRNA23392 packaged by Porphyromonas gingivalis outer membrane vesicles promotes oral squamous cell carcinomas migration and invasion by targeting desmocollin‐2 Molecular Oral Microbiology10.1111/omi.1233436:3(182-191)Online publication date: 4-Apr-2021
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cover image ACM Other conferences
SSDBM '14: Proceedings of the 26th International Conference on Scientific and Statistical Database Management
June 2014
417 pages
ISBN:9781450327220
DOI:10.1145/2618243
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 June 2014

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SSDBM '14

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SSDBM '14 Paper Acceptance Rate 26 of 71 submissions, 37%;
Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

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  • (2024)Viral microRNA in HPV16-associated cervical cancer: expression, diagnostic potential, and biological functionsRussian Journal of Oncology10.17816/onco63713329:3(183-194)Online publication date: 21-Dec-2024
  • (2024)Identification and characterization of host miRNAs that target the mouse mammary tumour virus (MMTV) genomeOpen Biology10.1098/rsob.24020314:12Online publication date: 11-Dec-2024
  • (2021) sRNA23392 packaged by Porphyromonas gingivalis outer membrane vesicles promotes oral squamous cell carcinomas migration and invasion by targeting desmocollin‐2 Molecular Oral Microbiology10.1111/omi.1233436:3(182-191)Online publication date: 4-Apr-2021
  • (2020)Efficient Calculation of Empirical P-values for Association Testing of Binary ClassificationsProceedings of the 32nd International Conference on Scientific and Statistical Database Management10.1145/3400903.3400923(1-12)Online publication date: 7-Jul-2020
  • (2019)MirLibSparkProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3343463(669-674)Online publication date: 4-Sep-2019
  • (2016)miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA–target interaction dataNucleic Acids Research10.1093/nar/gkw118545:6(e42-e42)Online publication date: 28-Nov-2016
  • (2016)Experimental verification of a predicted novel microRNA located in human PIK3CA gene with a potential oncogenic function in colorectal cancerTumor Biology10.1007/s13277-016-5264-y37:10(14089-14101)Online publication date: 10-Aug-2016
  • (2015)Hadoop Job Scheduling Based on Mixed Ant-Genetic AlgorithmProceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery10.1109/CyberC.2015.48(226-229)Online publication date: 17-Sep-2015

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