LEC-Codec: Learning-Based Genome Data Compression.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-03 DOI:10.1109/TCBB.2024.3473899
Zhenhao Sun, Meng Wang, Shiqi Wang, Sam Kwong
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引用次数: 0

Abstract

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.

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LEC-Codec:基于学习的基因组数据压缩
在本文中,我们提出了基于学习的 gEnome 编解码器 (LEC),其设计旨在提高效率和灵活性。LEC 集成了多项先进技术,包括基群(GoB)压缩、多线编码和双向预测,所有这些技术都旨在优化无损压缩中编码复杂性和性能之间的平衡。我们提出的编解码器中应用的模型是数据驱动的,基于深度神经网络来推断每个符号的概率,从而实现完全并行的编码和解码,并为不同的应用配置复杂度。基于压缩比和推理速度的一系列配置,实验结果表明,所提出的方法在压缩性能方面非常高效,并为实际应用提供了更大的灵活性。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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