  Multidimensional scaling of gene sequence data has long played a vital role
in analysing gene sequence data to identify clusters and patterns. However the
computation complexities and memory requirements of state-of-the-art
dimensional scaling algorithms make it infeasible to scale to large datasets.
In this paper we present an autoencoder-based dimensional reduction model which
can easily scale to datasets containing millions of gene sequences, while
attaining results comparable to state-of-the-art MDS algorithms with minimal
resource requirements. The model also supports out-of-sample data points with a
99.5%+ accuracy based on our experiments. The proposed model is evaluated
against DAMDS with a real world fungi gene sequence dataset. The presented
results showcase the effectiveness of the autoencoder-based dimension reduction
model and its advantages.
