  A common problem in bioinformatics is related to identifying gene regulatory
regions marked by relatively high frequencies of motifs, or deoxyribonucleic
acid sequences that often code for transcription and enhancer proteins.
Predicting alignment scores between subsequence k-mers and a given motif
enables the identification of candidate regulatory regions in a gene, which
correspond to the transcription of these proteins. We propose a one-dimensional
(1-D) Convolution Neural Network trained on k-mer formatted sequences
interspaced with the given motif pattern to predict pairwise alignment scores
between the consensus motif and subsequence k-mers. Our model consists of
fifteen layers with three rounds of a one-dimensional convolution layer, a
batch normalization layer, a dense layer, and a 1-D maximum pooling layer. We
train the model using mean squared error loss on four different data sets each
with a different motif pattern randomly inserted in DNA sequences: the first
three data sets have zero, one, and two mutations applied on each inserted
motif, and the fourth data set represents the inserted motif as a
position-specific probability matrix. We use a novel proposed metric in order
to evaluate the model's performance, $S_{\alpha}$, which is based on the
Jaccard Index. We use 10-fold cross validation to evaluate out model. Using
$S_{\alpha}$, we measure the accuracy of the model by identifying the 15
highest-scoring 15-mer indices of the predicted scores that agree with that of
the actual scores within a selected $\alpha$ region. For the best performing
data set, our results indicate on average 99.3% of the top 15 motifs were
identified correctly within a one base pair stride ($\alpha = 1$) in the out of
sample data. To the best of our knowledge, this is a novel approach that
illustrates how data formatted in an intelligent way can be extrapolated using
machine learning.
