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PseAAC specifications


Unique identifier OMICS_09961
Name PseAAC
Interface Web user interface
Restrictions to use None
Input data Protein sequences
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Shen H.B.

Publication for PseAAC

PseAAC citations


Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm

PLoS One
PMCID: 5896989
PMID: 29649330
DOI: 10.1371/journal.pone.0195636

[…] is still insufficient. subsequently, taking into account both amino acid composition information and amphipathic sequence-order information, chou et al. introduced the pseudo-amino acid composition (pseaac), and relevant experimental results proved that the discriminant performance of pseaac overmatched both aac and dipc partly [–]. afterwards, the position-specific scoring matrix (pssm) […]


An information based network approach for protein classification

PLoS One
PMCID: 5370107
PMID: 28350835
DOI: 10.1371/journal.pone.0174386

[…] remaining (non-primate) species (marsupialia: opossum and wallaroo; cetacea: fin whale and blue whale; rodentia: mouse and rat) are clustered around it., to compare with machine learning methods, pseaac features [] are extracted from the sequences and libsvm [] is applied to classify the proteins. in libsvm analysis, the 28 mammal species are classified into different biological orders. […]


In Silico Prediction of Gamma Aminobutyric Acid Type A Receptors Using Novel Machine Learning Based SVM and GBDT Approaches

Biomed Res Int
PMCID: 4992803
PMID: 27579307
DOI: 10.1155/2016/2375268

[…] amino acids composition and position, a gabaar classifier was first constructed using a 188-dimensional (188d) algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (pseaac) and protrweb web-based algorithms for human gabaar proteins. then, four classifiers including gradient boosting decision tree (gbdt), random forest (rf), a library for support vector machine […]


Human Protein Subcellular Localization with Integrated Source and Multi label Ensemble Classifier

Sci Rep
PMCID: 4914962
PMID: 27323846
DOI: 10.1038/srep28087

[…] wei’s secondary structure features, and pssm matrix features. several web servers were also developed for feature extraction of protein primary sequence, including pse-in-one, protrweb, and pseaac., proper classifier can help to improve the prediction performance. support vector machine (svm), k-nearest neighbor (knn), artificial neural network, random forest (rf), and ensemble learning […]


Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine Pairwise Algorithm Utilizing LZ Complexity

Biomed Res Int
PMCID: 4352747
PMID: 25802839
DOI: 10.1155/2015/212715

[…] concept cannot be performed on that particular sequence., to solve the problem of the sequence alignment, in [], they utilize the concept of amino acid composition and pseudo amino acid composition (pseaac) to represent the amps sequence. then, the maximum relevance minimum redundancy (mrmr) method [] and incremental feature selection (ifs) method [, ] are applied to select the optimal feature […]


Reverse Engineering of Genome wide Gene Regulatory Networks from Gene Expression Data

Curr Genomics
PMCID: 4412962
PMID: 25937810
DOI: 10.2174/1389202915666141110210634

[…] in a discrete model may completely lose all of the sequence-order information []. to avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition or chou’s pseaac was proposed [,]. ever since the concept of pseaac was proposed in 2001 [], the approach of representing protein/peptide sequences has been widely used in all the areas of computational […]

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PseAAC institution(s)
Gordon Life Science Institute, San Diego, CA, USA; Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China

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