Multidimensional Scaling for Gene Sequence Data with Autoencoders
This addresses scalability issues for researchers analyzing large gene sequence datasets, though it is incremental as it adapts existing autoencoder methods to a specific domain.
The paper tackles the computational and memory limitations of state-of-the-art multidimensional scaling algorithms for gene sequence data by proposing an autoencoder-based model that scales to millions of sequences with comparable results and minimal resource requirements, achieving 99.5%+ accuracy for out-of-sample data.
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.