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 Citation (Scopus)

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

Fingerprint

Statistical Models
MicroRNAs
Genes
Genome
Binding sites
Computational methods
Binding Sites
Messenger RNA
Screening
Gene Regulatory Networks
Human Body
Transcriptome
Protein Isoforms

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

A new statistical model for genome-scale MicroRNA target prediction. / Hakguder, Zeynep; Liao, Chunxiao; Shu, Jiang; Cui, Juan.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 101-107 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

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

Hakguder, Z, Liao, C, Shu, J & Cui, J 2017, A new statistical model for genome-scale MicroRNA target prediction. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 101-107, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217633
Hakguder Z, Liao C, Shu J, Cui J. A new statistical model for genome-scale MicroRNA target prediction. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu C-R, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 101-107. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). https://doi.org/10.1109/BIBM.2017.8217633
Hakguder, Zeynep ; Liao, Chunxiao ; Shu, Jiang ; Cui, Juan. / A new statistical model for genome-scale MicroRNA target prediction. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 101-107 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).
@inproceedings{e567775d2e1149c2bccd735a19a5316f,
title = "A new statistical model for genome-scale MicroRNA target prediction",
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.",
keywords = "Bayesian inference, Dirichlet Process Gaussian Mixture, Machine learning, MicroRNA, MicroRNA target prediction",
author = "Zeynep Hakguder and Chunxiao Liao and Jiang Shu and Juan Cui",
year = "2017",
month = "12",
day = "15",
doi = "10.1109/BIBM.2017.8217633",
language = "English (US)",
series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "101--107",
editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

}

TY - GEN

T1 - A new statistical model for genome-scale MicroRNA target prediction

AU - Hakguder, Zeynep

AU - Liao, Chunxiao

AU - Shu, Jiang

AU - Cui, Juan

PY - 2017/12/15

Y1 - 2017/12/15

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Dirichlet Process Gaussian Mixture

KW - Machine learning

KW - MicroRNA

KW - MicroRNA target prediction

UR - http://www.scopus.com/inward/record.url?scp=85045965204&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045965204&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2017.8217633

DO - 10.1109/BIBM.2017.8217633

M3 - Conference contribution

AN - SCOPUS:85045965204

T3 - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

SP - 101

EP - 107

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

A2 - Yoo, Illhoi

A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

PB - Institute of Electrical and Electronics Engineers Inc.

ER -