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We present a physichemical encoding method that maps protein sequences into feature vectors composed of the locations and lengths of amino acid groups (AAGs) ...
Abstract. Computational prediction of protein localization is one common way to characterize the functions of newly sequenced proteins. Sequence features.
Abstract. Computational prediction of protein localization is one common way to characterize the functions of newly sequenced proteins. Sequence features.
Hu, J. & Zhang, F. (2009). Improving protein localization prediction using amino acid group based physiochemical encoding. Lecture Notes in Computer Science / ...
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It is shown that AAG based features are able to achieve higher prediction accuracy than the widely used AA composition and AA pair composition to ...
Bibliographic details on Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding.
Oct 29, 2021 · As such, this study attempted to use the composition and chemical properties of the protein surface to represent the proteins. The surface amino ...
Dec 2, 2022 · We present a method that improves subcellular localization prediction for proteins based on their sequence by leveraging structure prediction ...
Missing: Group Physichemical
Dec 2, 2022 · We present a method that improves subcellular localization prediction for proteins based on their sequence by leveraging structure prediction ...
Missing: Group Physichemical
Mar 6, 2019 · Lin et al. refined the PseAAC based on the physico-chemical characteristics of the 20 amino acids, and adopted SVM to predict protein ...
Missing: Physichemical | Show results with:Physichemical