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

ABSTRACT:

Background

Human 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.

Results

In 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. Comput

SUBMITTER: Liu P 

PROVIDER: S-EPMC7877110 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature