Kusum Kumari Singh

This study compares deep learning models for identifying canonical and non-canonical splice sites across three species, finding that SpliceViNCI performs best, especially with multi-species training.

This paper introduces SpliceViNCI, a model using BLSTM to identify non-canonical splice junctions and visualizes its learned features using integrated gradient and occlusion methods.

The paper identifies and analyzes alternate protein isoforms of MAGOH and MAGOHB, revealing evolutionary conservation and differences in key amino acid residues.

This paper evaluates visualization techniques for interpreting a neural network's learned features in splice junction prediction, finding that perturbation-based methods outperform back-propagation for canonical motifs.

SpliceVec uses distributed feature representations to improve splice junction prediction by avoiding manual feature extraction and achieving better classification performance.