A novel RNA pseudouridine site prediction model using Utility Kernel and data-driven parameters

• URL: http://arxiv.org/abs/2311.16132v1
• Date: Thu, 2 Nov 2023 08:32:10 GMT
• Title: A novel RNA pseudouridine site prediction model using Utility Kernel and data-driven parameters
• Authors: Sourabh Patil, Archana Mathur, Raviprasad Aduri, Snehanshu Saha
• Abstract summary: Pseudouridine is the most frequent modification in RNA. Existing models to predict the pseudouridine sites in a given RNA sequence mainly depend on user-defined features. We propose a Support Vector Machine (SVM) Kernel based on utility theory from Economics.
• Score: 0.7373617024876725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: RNA protein Interactions (RPIs) play an important role in biological systems. Recently, we have enumerated the RPIs at the residue level and have elucidated the minimum structural unit (MSU) in these interactions to be a stretch of five residues (Nucleotides/amino acids). Pseudouridine is the most frequent modification in RNA. The conversion of uridine to pseudouridine involves interactions between pseudouridine synthase and RNA. The existing models to predict the pseudouridine sites in a given RNA sequence mainly depend on user-defined features such as mono and dinucleotide composition/propensities of RNA sequences. Predicting pseudouridine sites is a non-linear classification problem with limited data points. Deep Learning models are efficient discriminators when the data set size is reasonably large and fail when there is a paucity of data ($<1000$ samples). To mitigate this problem, we propose a Support Vector Machine (SVM) Kernel based on utility theory from Economics, and using data-driven parameters (i.e. MSU) as features. For this purpose, we have used position-specific tri/quad/pentanucleotide composition/propensity (PSPC/PSPP) besides nucleotide and dineculeotide composition as features. SVMs are known to work well in small data regimes and kernels in SVM are designed to classify non-linear data. The proposed model outperforms the existing state-of-the-art models significantly (10%-15% on average).

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