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A Comprehensive Review of Deep Learning-based Variant Calling Methods by Ren Junjun; Zhang Zhengqian; Wu Ying; Wang Jialiang; Liu Yongzhuang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is A Comprehensive Review of Deep Learning-based Variant Calling Methods about?

## Abstract Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning–based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning–based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Who reads A Comprehensive Review of Deep Learning-based Variant Calling Methods?

Author

Ren Junjun; Zhang Zhengqian; Wu Ying; Wang Jialiang; Liu Yongzhuang

Publisher

Oxford University Press (OUP)

Published

2024

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)