  Thanks to rapidly evolving sequencing techniques, the amount of genomic data
at our disposal is growing increasingly large. Determining the gene structure
is a fundamental requirement to effectively interpret gene function and
regulation. An important part in that determination process is the
identification of translation initiation sites. In this paper, we propose a
novel approach for automatic prediction of translation initiation sites,
leveraging convolutional neural networks that allow for automatic feature
extraction. Our experimental results demonstrate that we are able to improve
the state-of-the-art approaches with a decrease of 75.2% in false positive rate
and with a decrease of 24.5% in error rate on chosen datasets. Furthermore, an
in-depth analysis of the decision-making process used by our predictive model
shows that our neural network implicitly learns biologically relevant features
from scratch, without any prior knowledge about the problem at hand, such as
the Kozak consensus sequence, the influence of stop and start codons in the
sequence and the presence of donor splice site patterns. In summary, our
findings yield a better understanding of the internal reasoning of a
convolutional neural network when applying such a neural network to genomic
data.
