A novel RNA pseudouridine site prediction model using Utility Kernel and
  data-driven parameters
Sourabh Patil Archana Mathur Raviprasad Aduri Snehanshu Saha
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).