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Pse-Analysis

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Expedites the pace in conducting genome/proteome analysis. Pse-Analysis can automatically complete the following five procedures: (1) sample feature extraction, (2) optimal parameter selection, (3) model training, (4) cross validation, and (5) evaluating prediction quality. The tool will automatically construct an ideal predictor, followed by yielding the predicted results for the submitted query samples.

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Pse-Analysis classification

Pse-Analysis specifications

Software type:
Package/Module
Restrictions to use:
None
Operating system:
Unix/Linux, Windows
License:
BSD 3-clause “New” or “Revised” License
Version:
1.0
Maintained:
Yes
Interface:
Command line interface
Input data:
Benchmark dataset and query biological sequences.
Programming languages:
Python
Computer skills:
Advanced
Stability:
Stable

Pse-Analysis distribution

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Pse-Analysis support

Documentation

Maintainer

  • Bin Liu <>

Credits

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Publications

Institution(s)

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Gordon Life Science Institute, Boston, MA, USA; School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China; Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China

Funding source(s)

This work was supported by National Natural Science Foundation of China (No. 61672184, 61300112, and 61573118), the Natural Science Foundation of Guangdong Province (2014A030313695), Guangdong Natural Science Funds for Distinguished Young Scholars (2016A030306008), and Scientific Research Foundation in Shenzhen (Grant No. JCYJ20150626110425228).

Link to literature

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