  Non protein coding regions of the human genome contain many complex patterns
which regulate the cellular activity. Studying the human genome is limited by
the lack of understanding of its features and their complex interactions.
However, recent advances in AI research have enabled automatically learning
representations of high dimensional complex data without feature engineering,
using deep neural networks. Therefore, in this paper, we demonstrate that a
convolutional neural network can learn a representation of DNA sequence without
specifying any motifs or patterns, such that it becomes capable of predicting
whether a DNA sequence is natural or artificial. The trained model could
distinguish scrambled vs real DNA sequences for scrambling lengths of 2 bp, 10
bp, 50 bp and even 100 bp, with a significantly higher accuracy than linear
SVMs. With this study, we have discovered that regions of non protein coding
DNA might have meaningful interactions at even longer than 100 bp distances
even though they do not code proteins.
