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Pse-Analysis specifications


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




No version available



  • person_outline Bin Liu

Publication for Pse-Analysis

Pse-Analysis citations


A Novel Modeling in Mathematical Biology for Classification of Signal Peptides

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

[…] chine 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 fu […]


On the prediction of DNA binding proteins only from primary sequences: A deep learning approach

PLoS One
PMCID: 5747425
PMID: 29287069
DOI: 10.1371/journal.pone.0188129

[…] eir qualitative and quantitative descriptions, of amino acids for predicting protein interactions []. Also there are several general purpose protein feature extraction tools such as Pse-in-One [] and Pse-Analysis []. They generated feature vectors by a user-defined schema and make them more flexible.Deep learning is now one of the most active fields in machine learning and has achieved big success […]


Sparse Bayesian classification and feature selection for biological expression data with high correlations

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

[…] ds 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. […]


Reconstructing evolutionary trees in parallel for massive sequences

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

[…] they 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. […]


Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

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 in Pse-Ana […]


Automated prediction of emphysema visual score using homology based quantification of low attenuation lung region

PLoS One
PMCID: 5444793
PMID: 28542398
DOI: 10.1371/journal.pone.0178217

[…] g the concatenation of Betti numbers obtained from binarized CT images at the various threshold levels. The method of the current study is similar to those used in bioinformatics, such as Pse-in-One, Pse-Analysis, repDNA, and iDHS-EL [–]. These studies and the current study focused on how to create the feature vector which can be easily and effectively combined with machine learning algorithm. […]


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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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