Human mitochondrial genome compression using machine learning techniques.

Journal: Human genomics

Published Date: Oct 22, 2019

Abstract

BACKGROUND: In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effectively compression genome sequences data remains an unsolved problem.

Authors

Rongjie Wang

Peng Cheng Laboratory, ShenZhen, China.
Tianyi Zang

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. tianyi.zang@hit.edu.cn.
Yadong Wang

The Biofoundry, Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States.

Keywords

Algorithms Base Sequence Data Compression Databases, Genetic Genome, Mitochondrial Humans Machine Learning Models, Genetic Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (31639043)

Human mitochondrial genome compression using machine learning techniques.

Abstract

Authors

Keywords

External Resources

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