Pse-Analysis protocols

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Pse-Analysis specifications

Information


Unique identifier OMICS_14857
Name Pse-Analysis
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Benchmark dataset and query biological sequences.
Operating system Unix/Linux, Windows
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes

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  • person_outline Bin Liu <>

Publication for Pse-Analysis

Pse-Analysis in pipeline

2018
PMCID: 5773712
PMID: 29348418
DOI: 10.1038/s41598-018-19491-y

[…] learning classifiers (ann, random forest) have been widely used in the field of bioinformatics, and some predictors have been established based on these classifiers, such as psfm-dbt, 2l-pirna, pse-analysis, protdec-ltr, protdec-ltr2.0, etc. some powerful protein analysis methods have been proposed for the formulation of biological sequences, such as pse-in-one, repdna, based on different […]


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Pse-Analysis in publications

 (10)
PMCID: 5773712
PMID: 29348418
DOI: 10.1038/s41598-018-19491-y

[…] learning classifiers (ann, random forest) have been widely used in the field of bioinformatics, and some predictors have been established based on these classifiers, such as psfm-dbt, 2l-pirna, pse-analysis, protdec-ltr, protdec-ltr2.0, etc. some powerful protein analysis methods have been proposed for the formulation of biological sequences, such as pse-in-one, repdna, based on different […]

PMCID: 5744982
PMID: 29281700
DOI: 10.1371/journal.pone.0189541

[…] such as support vector machine (svm) [], linear discriminant analysis [] and gaussian naive bayes []. many tools are particularly designed for biological data. for example, a python package called pse-analysis [], is developed to automatically generate classifiers for genomics and proteomics datasets. it is based on the framework of libsvm [] and inherits the characteristics of the svm method. […]

PMCID: 5751538
PMID: 29297337
DOI: 10.1186/s12918-017-0476-3

[…] would be applied to protein, rna, and dna sequence analysis []. recently, some algorithms have been proposed to extraction the evolutionary information from multiple sequence alignments, such as pse-analysis [], and pseudo proteins []. future studies will focus on extracting features from the evolutionary information., not applicable., publication costs were funded by the natural science […]

PMCID: 5728509
PMID: 29236759
DOI: 10.1371/journal.pone.0189533

[…] these machine learning pathways, support vector machine (svm) and k-nearest neighbor (knn) are used to study performance [–]. to facilitate a more flexible and comprehensive analysis, pse-in-one and pse-analysis have been proposed. these methods are considered powerful bioinformatics analysis tools based on web server and python package, respectively [, ]. these tools can generate any desired […]

PMCID: 5704239
PMID: 29149087
DOI: 10.3390/genes8110326

[…] it can easily incorporate machine learning algorithms such as svm and knn, which were not capable of handling anything except vectors back then. in [], the authors developed a python package called “pse-analysis”, which can automatically perform feature extraction and selection, parameter tuning, model training, cross-validation and evaluation for any biological sequences. the training […]


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Pse-Analysis 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
Pse-Analysis 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).

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