Abstract
In this paper, we propose a pattern-based protein function annotation framework, employing protein interaction networks, to predict annotation functions of proteins. More specifically, we first detect patterns that appear in the neighborhood of proteins with a particular functionality, and then transfer annotations between two proteins only if they have similar annotation patterns. We show that, in comparison with other techniques, our approach predicts protein annotations more effectively. Our technique (a) produces the highest prediction accuracy of 70-80% precision and recall for different organism specific datasets, and (b) is robust to false positives in protein interaction networks.
Keywords
- Biological Network
- Protein Interaction Network
- Alignment Score
- Protein Function Prediction
- Protein Interaction Data
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Kirac, M., Ozsoyoglu, G. (2008). Protein Function Prediction Based on Patterns in Biological Networks. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_18
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DOI: https://doi.org/10.1007/978-3-540-78839-3_18
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