Abstract
Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.
| Original language | English |
|---|---|
| Pages (from-to) | 2499-2502 |
| Number of pages | 4 |
| Journal | Bioinformatics |
| Volume | 34 |
| Issue number | 14 |
| DOIs | |
| Publication status | Published - 15 Jul 2018 |
Cite this
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iFeature : a python package and web server for features extraction and selection from protein and peptide sequences. / Chen, Zhen; Zhao, Pei; Li, Fuyi; Leier, Andre; Marquez-Lago, Tatiana; Wang, Yanan; Webb, Geoffrey I.; Smith, A. Ian; Daly, Roger J.; Chou, Kuo-Chen; Song, Jiangning.
In: Bioinformatics, Vol. 34, No. 14, 15.07.2018, p. 2499-2502.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - iFeature
T2 - a python package and web server for features extraction and selection from protein and peptide sequences
AU - Chen, Zhen
AU - Zhao, Pei
AU - Li, Fuyi
AU - Leier, Andre
AU - Marquez-Lago, Tatiana
AU - Wang, Yanan
AU - Webb, Geoffrey I.
AU - Smith, A. Ian
AU - Daly, Roger J.
AU - Chou, Kuo-Chen
AU - Song, Jiangning
PY - 2018/7/15
Y1 - 2018/7/15
N2 - Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.
AB - Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.
UR - http://www.scopus.com/inward/record.url?scp=85052948094&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/bty140
DO - 10.1093/bioinformatics/bty140
M3 - Article
AN - SCOPUS:85052948094
VL - 34
SP - 2499
EP - 2502
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 14
ER -