Affordable Access

Access to the full text

ReCGBM: a gradient boosting-based method for predicting human dicer cleavage sites

Authors
  • Liu, Pengyu1
  • Song, Jiangning2
  • Lin, Chun-Yu3, 3
  • Akutsu, Tatsuya1
  • 1 Kyoto University, Kyoto, 611-0011, Japan , Kyoto (Japan)
  • 2 Monash University, Melbourne, VIC, 3800, Australia , Melbourne (Australia)
  • 3 National Chiao Tung University, Hsinchu, 300, Taiwan , Hsinchu (Taiwan)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Feb 10, 2021
Volume
22
Issue
1
Identifiers
DOI: 10.1186/s12859-021-03993-0
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundHuman dicer is an enzyme that cleaves pre-miRNAs into miRNAs. Several models have been developed to predict human dicer cleavage sites, including PHDCleav and LBSizeCleav. Given an input sequence, these models can predict whether the sequence contains a cleavage site. However, these models only consider each sequence independently and lack interpretability. Therefore, it is necessary to develop an accurate and explainable predictor, which employs relations between different sequences, to enhance the understanding of the mechanism by which human dicer cleaves pre-miRNA.ResultsIn this study, we develop an accurate and explainable predictor for human dicer cleavage site – ReCGBM. We design relational features and class features as inputs to a lightGBM model. Computational experiments show that ReCGBM achieves the best performance compared to the existing methods. Further, we find that features in close proximity to the center of pre-miRNA are more important and make a significant contribution to the performance improvement of the developed method.ConclusionsThe results of this study show that ReCGBM is an interpretable and accurate predictor. Besides, the analyses of feature importance show that it might be of particular interest to consider more informative features close to the center of the pre-miRNA in future predictors.

Report this publication

Statistics

Seen <100 times