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DEEPre

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Provides end-to-end feature extraction and classifier training method for enzyme function prediction. DEEPre is based on deep learning and utilizes the sequence information. It forces the model to learn to extract features by itself and adapt the parameters of the classifier simultaneously so that it can improve the performance in a virtuous circle. This tool predicts a score for each candidate value of a certain type II restriction enzyme (EC) digit, and can serve to detect the enzyme promiscuity.

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DEEPre classification

DEEPre specifications

Interface:
Web user interface
Input data:
A raw sequence encoding.
Stability:
Stable
Maintained:
Yes
Restrictions to use:
None
Computer skills:
Basic
Source code URL:
http://www.cbrc.kaust.edu.sa/DEEPre/download.php

DEEPre support

Maintainers

  • Xin Gao <>
  • Yu Li <>

Additional information

http://www.cbrc.kaust.edu.sa/DEEPre/information.html#format

Credits

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Publications

Institution(s)

King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia; Illinois Institute of Technology, Computer Science Department, Chicago, IL, USA; Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China

Funding source(s)

Supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No URF/1/1976- 04 and URF/1/3007-01, National Natural Science Foundation of China (61401131 and 61731008).

Link to literature

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