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A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

Machine learning methods are increasingly being applied to model and predict biomolecular interactions, while efficient feature representation plays a vital role. To this end, a unified biological sequence deep representation learning framework BioSeq2vec is proposed to extract discriminative features of any type of biological sequence. For arbitrary-length sequence input, the BioSeq2vec produces fixed-length efficient feature representation, which can be applied to various learning models. The performance of BioSeq2vec is evaluated on lncRNA-protein interaction prediction tasks. Experimental results reveal the superior performance of BioSeq2vec in biological sequence feature representation and broad prospects in various genome informatics and computational biology studies.

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Acknowledgement

HCY, ZHY and XRS designed, conceived the algorithm, carried out analyses, prepared the data sets, carried out experiments, and wrote the manuscript; DSH, ZHG performed and analyzed experiments and wrote the manuscript; All authors read and approved the final manuscript.

Funding

This work is supported by the National Outstanding Youth Science Foundation of NSFC, under grant 61722212, and the National Natural Science Foundation of China, under grants 61873212, 61861146002, and 61732012.

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Authors and Affiliations

  1. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Hai-Cheng Yi, Zhu-Hong You, Xiao-Rui Su & Zhen-Hao Guo

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hai-Cheng Yi, Zhu-Hong You, Xiao-Rui Su & Zhen-Hao Guo

  3. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    De-Shuang Huang

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  1. Hai-Cheng Yi
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  2. Zhu-Hong You
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  3. Xiao-Rui Su
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  5. Zhen-Hao Guo
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Correspondence to Zhu-Hong You .

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Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Yi, HC., You, ZH., Su, XR., Huang, DS., Guo, ZH. (2020). A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_30

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_30

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