@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",
note = "Funding Information: The authors would like to thank all SBBI members who have been involved in this work for providing helpful discussions and technical assistance. This research is support by the NIH funded COBRE grant (1P20GM104320), Food for health seed grant, and the Tobacco Settlement Fund as part of Cui{\textquoteright}s startup grant support. No conflict of interest declared. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",
year = "2017",
month = dec,
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",
}