Identifying anticancer peptides by using a generalized chaos game representation

DOI10.1007/s00285-018-1279-xzbMath1410.92083OpenAlexW2895742595WikidataQ57022254 ScholiaQ57022254MaRDI QIDQ1738032

Matthias Dehmer, Li Ge, Jiaguo Liu, Yusen Zhang

Publication date: 29 March 2019

Published in: Journal of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00285-018-1279-x

zbMATH Keywords

support vector machinesimilarity analysischaos game representationanticancer peptides

Mathematics Subject Classification ID

Applications of game theory (91A80) Protein sequences, DNA sequences (92D20) Computational methods for problems pertaining to biology (92-08)

Uses Software

• ape
• LIBSVM
• e1071
• IsoRank
• ClustalW
• Cd-hit
• iACP

Cites Work

• A new method for analyzing H5N1 avian influenza virus
• Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation
• Comparative analysis of protein primary sequences with graph energy
• A novel representation for apoptosis protein subcellular localization prediction using support vector machine
• A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
• A 3D graphical representation of protein sequences based on the Gray code
• Support-vector networks
• Chaos game representation of protein sequences based on the detailed HP model and their multifractal and correlation analyses
• Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via ames test

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