Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach
DOI10.1016/J.JTBI.2016.08.027zbMath1405.92219OpenAlexW2518228425WikidataQ51542675 ScholiaQ51542675MaRDI QIDQ1664426
Publication date: 27 August 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2016.08.027
support vector machinesSVMconfusion matrixhybrid approachesROC analysishemoglobinPSSMposition specific scoring matrixamphibians hemoglobinAves hemoglobinfish hemoglobinmammalian hemoglobinreptiles hemoglobinvertebrate hemoglobin
General biostatistics (92B15) Problems related to evolution (92D15) Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20)
Uses Software
Cites Work
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
- Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition
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- Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition
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- Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition
- Prediction of \(\beta\)-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine
- Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model
- Time-dependent scattering of generalized plane waves by a wedge
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