A new statistical model for genome-scale MicroRNA target prediction

Zeynep Hakguder, Chunxiao Liao, Jiang Shu, Juan Cui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

MicroRNAs regulate virtually the whole gene network in human body and have been implicated in most physiological and pathological conditions including cancers. Understanding the precise mechanisms of microRNA-mRNA interaction is fundamentally important to elucidate the important roles of miRNA in regulating various cellular and disease developmental stages. Numerous computational methods have been developed for miRNA target prediction, mostly focusing on static binding prediction and highly dependent on sequence-pairing interactions. However, the interplay between competing and cooperative microRNA-target binding makes it exceptionally complex and challenging for reliable target identification, which has hindered the existing tools from practical use. In this study, we present a new computational method for microRNA target prediction using the Dirichlet Process Gaussian Mixture Model (DPGMM). A comprehensive collection of features related to sequence and structure of microRNAs, mRNAs, and the binding sites have been assessed to optimize the statistical prediction of new binding sites in human transcriptome. Through multiple evaluations on recently-discovered miRNA-mRNA interactions reported in large-scale sequencing analyses and a screening test on the entire human transcripts, the results show that our model outperformed several state-of-the-art tools in terms of reduced false positive prediction and promising predictive power on binding sites specific to transcript isoforms.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages101-107
Number of pages7
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

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Keywords

  • Bayesian inference
  • Dirichlet Process Gaussian Mixture
  • Machine learning
  • MicroRNA
  • MicroRNA target prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Hakguder, Z., Liao, C., Shu, J., & Cui, J. (2017). A new statistical model for genome-scale MicroRNA target prediction. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 101-107). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217633