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


There is no abstract available for this article.


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).


Appendixes A–C.


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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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