Using bootstrapping to identify protein locations
نویسندگان
چکیده
منابع مشابه
Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks
Background: Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from pro...
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A novel method was introduced to predict protein subcellular locations from sequences. Using sequence data, this method achieved a prediction accuracy higher than previous methods based on the amino acid composition. For three subcellular locations in a prokaryotic organism, the overall prediction accuracy reached 89.1%. For eukaryotic proteins, prediction accuracies of 73.0% and 78.7% were att...
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MOTIVATION Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. RESULTS In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction i...
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ژورنال
عنوان ژورنال: Proceedings of the American Society for Information Science and Technology
سال: 2012
ISSN: 0044-7870
DOI: 10.1002/meet.14504901137