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*24 May 2018*

Keywords:

encoding scheme, data representation, feature learning, deep learning, genomic signal processing, machine learning, genome analysis
Cite this article:

Yu N, Li Z, Yu Z.
Survey on Encoding Schemes for Genomic Data Representation and Feature Learning—From Signal Processing to Machine Learning.
Big Data Mining and Analytics,
2018, 1(3): 191-210.
https://doi.org/10.26599/BDMA.2018.9020018
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Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing (GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect, analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.

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Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing (GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect, analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.

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Acknowledgements

Rights and permissions

Received: 21 January 2018

Accepted: 24 January 2018

Published:
24 May 2018

Issue date: September 2018

© The author(s) 2018

We appreciate the supports from the Department of Computing Sciences, State University of New York College at Brockport.