Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC
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Publication:1717296
DOI10.1016/j.jtbi.2018.11.012zbMath1406.92212OpenAlexW2900490197WikidataQ93212963 ScholiaQ93212963MaRDI QIDQ1717296
Jijun Tang, Yinan Shen, Fei Guo
Publication date: 5 February 2019
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2018.11.012
Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40) Cell biology (92C37)
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Cites Work
- Unnamed Item
- ML-KNN: A lazy learning approach to multi-label learning
- Some remarks on protein attribute prediction and pseudo amino acid composition
- Multiple kernel clustering based on centered kernel alignment
- Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins
- \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
- Predicting Gram-positive bacterial protein subcellular localization based on localization motifs
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