An Overview of the Application of Deep Learning in Short-Read Sequence Classification

Kristaps Bebris, Inese Polaka


Advances in sequencing technology have led to an ever increasing amount of available short-read sequencing data. This has, consequently, exacerbated the need for efficient and precise classification tools that can be used in the analysis of these data. As it stands, recent years have shown that massive leaps in performance can be achieved when it comes to approaches that are based on heuristics, and apart from these improvements there has been an ever increasing interest in applying deep learning techniques to revolutionize this classification task. We attempt to study these approaches and to evaluate their performance in a reproducible fashion to get a better perspective on the current state of deep learning based methods when it comes to the classification of short-read sequencing data


Bioinformatics; Computational Biology; Machine Learning

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DOI: 10.7250/itms-2020-0005


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