  A single gene can encode for different protein versions through a process
called alternative splicing. Since proteins play major roles in cellular
functions, aberrant splicing profiles can result in a variety of diseases,
including cancers. Alternative splicing is determined by the gene's primary
sequence and other regulatory factors such as RNA-binding protein levels. With
these as input, we formulate the prediction of RNA splicing as a regression
task and build a new training dataset (CAPD) to benchmark learned models. We
propose discrete compositional energy network (DCEN) which leverages the
hierarchical relationships between splice sites, junctions and transcripts to
approach this task. In the case of alternative splicing prediction, DCEN models
mRNA transcript probabilities through its constituent splice junctions' energy
values. These transcript probabilities are subsequently mapped to relative
abundance values of key nucleotides and trained with ground-truth experimental
measurements. Through our experiments on CAPD, we show that DCEN outperforms
baselines and ablation variants.
