Multidimensional Scaling for Gene Sequence Data with Autoencoders

• URL: http://arxiv.org/abs/2104.09014v1
• Date: Mon, 19 Apr 2021 02:14:17 GMT
• Title: Multidimensional Scaling for Gene Sequence Data with Autoencoders
• Authors: Pulasthi Wickramasinghe, Geoffrey Fox
• Abstract summary: We present an autoencoder-based dimensional reduction model which can easily scale to datasets containing millions of gene sequences. The proposed model is evaluated against DAMDS with a real world fungi gene sequence dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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