.png){kind=icon}
.png){kind=icon}
Return
LEC-Codec: Learning-Based Genome Data Compression
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2024.3473899.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidirectional prediction, all of which are aimed at optimizing the balance between coding complexity and performance in lossless compression. The model applied in our proposed codec is data-driven, based on deep neural networks to infer probabilities for each symbol, enabling fully parallel encoding and decoding with configured complexity for diverse applications. Based upon a set of configurations on compression ratios and inference speed, experimental results show that the proposed method is very efficient in terms of compression performance and provides improved flexibility in real-world applications.
Keywords:
Genomics
Bioinformatics
Encoding
Context modeling
Symbols
Predictive models
Codecs
Computational modeling
Complexity theory
Termination of employment
Data compression
learning-based method
lossless genome compression
non-reference method
Journal
I
IF:
3.4
Papers:
3.3K
Citations:
6.4K