Deep Learning Based NLP Embedding Approach for Biosequence Classification

Biological sequence analysis involves the study of structural characteristics and chemical composition of a sequence. From a computational perspective, the goal is to represent sequences using vectors which bring out the essential features of the virus and enable efficient classification. Methods such as one-hot encoding, Word2Vec models, etc. have been explored for embedding sequences into the Euclidean plane. But these methods either fail to capture similarity information between k-mers or face the challenge of handling Out-of-Vocabulary (OOV) k-mers. In order to overcome these challenges, in this paper we aim explore the possibility of embedding Biosequences of MERS, SARS and SARS-CoV-2 using Global Vectors (GloVe) model and FastText n-gram representation. We conduct an extensive study to evaluate their performance using classical Machine Learning algorithms and Deep Learning methods. We compare our results with dna2vec, which is an existing Word2Vec approach. Experimental results show that FastText n-gram based sequence embeddings enable deeper insights into understanding the composition of each virus and thus give a classification accuracy close to 1. We also provide a study regarding the patterns in the viruses and support our results using various visualization techniques.