A Systematic Benchmark of Machine Learning Methods for Protein-RNA Interaction Prediction

Abstract

RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.

PB-LKS: a python package for predicting phage–bacteria interaction through local K-mer strategy

“…In addition to the dataset, selecting a suitable model is also important for the construction of the PB-LKS model. Here, we tested several widely used machine learning models and deep learning models that used to predict genome or protein interactions [ 42 ]. It is testified that the performance of the DL-based model is inferior to that of the tree-based model.…”