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SCIENCE CHINA Information Sciences, Volume 62, Issue 1: 019103(2019) https://doi.org/10.1007/s11432-018-9558-2

Identifying RNA-binding proteins using multi-label deep learning

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  • ReceivedApr 10, 2018
  • AcceptedAug 16, 2018
  • PublishedDec 17, 2018

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61725302, 61671288, 61603161, 61462018, 6176- 2026, 81500351), Science and Technology Commission of Shanghai Municipality (Grant Nos. 16JC1404- 300, 17JC1403500), Jiangsu Province's Young Medical Talents Project (Grant No. QNRC2016842), and “5123 Talents Project" of Affiliated Hospital of Jiangsu University (Grant No. 51232017305).


Supplement

Appendixes A–C.


References

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  • Figure 1

    (Color online) The flowchart of iDeepM. iDeepM first converts RNA sequence into one-hot encoded matrix, which is further fed into a CNN, followed by LSTM to learn label dependency under multi-label learning framework.

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