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A systematic benchmark of machine learning methods for protein-RNA interaction prediction.
Brief. Bioinform. 24:bbad307 (2023)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Rna Biology ; Rna-binding Proteins ; Benchmark ; Deep Learning
ISSN (print) / ISBN
1467-5463
e-ISSN
1477-4054
Journal
Briefings in Bioinformatics
Quellenangaben
Volume: 24,
Issue: 5,
Article Number: bbad307
Publisher
Oxford University Press
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)