Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes

• Published in: Biology (Basel, Switzerland) Vol. 12; no. 10; p. 1276
• Main Authors: Vaculík, Ondřej, Chalupová, Eliška, Grešová, Katarína, Majtner, Tomáš, Alexiou, Panagiotis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25-09-2023; MDPI
• Subjects: Binding proteins; Binding sites; CLIP-seq; Conserved sequence; Datasets; Deep learning; Evolutionary conservation; Genomes; interpretation; Localization; Nervous system diseases; Neural networks; Neurodegenerative diseases; Nucleotide sequence; Prediction models; Protein binding; Proteins; Ribonucleic acid; RNA; RNA-binding protein; Transfer learning; Germany
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology12101276

RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein–RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.