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Prediction of protein subcellular localization based on primary sequence data
Date
2003-01-01
Author
Ozarar, M
Atalay, Mehmet Volkan
Atalay, Rengül
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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This paper describes a system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order. Our approach for prediction is to find the most frequent motifs for each protein (class) based on clustering and then to use these most frequent motifs as features for classification. This approach allows a classification independent of the length of the sequence. Another important property of the approach is to provide a means to perform reverse analysis and analysis to extract rules. In addition to these and more importantly, we describe the use of a new encoding scheme for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. We present preliminary results of our system on a two class (dichotomy) classifier. However, it can be extended to multiple classes with some modifications.
Subject Keywords
Proteins
,
Amino acids
,
Organisms
,
Self organizing feature maps
,
Multilayer perceptrons
,
Encoding
,
Biological information theory
,
Genetic mutations
,
Matrices
,
System testing
URI
https://hdl.handle.net/11511/52956
Journal
COMPUTER AND INFORMATION SCIENCES - ISCIS 2003
Collections
Department of Computer Engineering, Article
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Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to nd the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features...
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Prediction of protein subcellular localization based on primary sequence data
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M. Ozarar, M. V. Atalay, and R. Atalay, “Prediction of protein subcellular localization based on primary sequence data,”
COMPUTER AND INFORMATION SCIENCES - ISCIS 2003
, pp. 611–618, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52956.