Align-gram : Rethinking the Skip-gram Model for Protein Sequence Analysis

• URL: http://arxiv.org/abs/2012.03324v1
• Date: Sun, 6 Dec 2020 17:04:17 GMT
• Title: Align-gram : Rethinking the Skip-gram Model for Protein Sequence Analysis
• Authors: Nabil Ibtehaz, S. M. Shakhawat Hossain Sourav, Md. Shamsuzzoha Bayzid, M. Sohel Rahman
• Abstract summary: We propose a novel embedding scheme, Align-gram, which is capable of mapping the similar $k$-mers close to each other in a vector space. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.
• Score: 0.8733639720576208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the `language of life', has been analyzed for a multitude of applications and inferences. Motivation: Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. Results: We propose a novel $k$-mer embedding scheme, Align-gram, which is capable of mapping the similar $k$-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.

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