Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques

Background: Cis-regulatory regions (CRRs) are non-coding regions of the DNA that
fine control the spatio-temporal pattern of transcription; they are involved in a wide
range of pivotal processes such as the development of specific cell-lines/tissues and
the dynamic cell response to physiological stimuli. Recent studies showed that genetic
variants occurring in CRRs are strongly correlated with pathogenicity or deleterious-
ness. Considering the central role of CRRs in the regulation of physiological and patho-
logical conditions, the correct identification of CRRs and of their tissue-specific activity
status through Machine Learning methods plays a major role in dissecting the impact
of genetic variants on human diseases. Unfortunately, the problem is still open, though
some promising results have been already reported by (deep) machine-learning based
methods that predict active promoters and enhancers in specific tissues or cell lines by
encoding epigenetic or spectral features directly extracted from DNA sequences.
Results: We present the experiments we performed to compare two Deep Neural
Networks, a Feed-Forward Neural Network model working on epigenomic features,
and a Convolutional Neural Network model working only on genomic sequence,
targeted to the identification of enhancer- and promoter-activity in specific cell lines.
While performing experiments to understand how the experimental setup influences
the prediction performance of the methods, we particularly focused on (1) automatic
model selection performed by Bayesian optimization and (2) exploring different data
rebalancing setups for reducing negative unbalancing effects.
Conclusions: Results show that (1) automatic model selection by Bayesian optimiza-
tion improves the quality of the learner; (2) data rebalancing considerably impacts the
prediction performance of the models; test set rebalancing may provide over-optimis-
tic results, and should therefore be cautiously applied; (3) despite working on sequence
data, convolutional models obtain performance close to those of feed forward models
working on epigenomic information, which suggests that also sequence data carries informative content for CRR-activity prediction. We therefore suggest combining both models/data types in future works.