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Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach by H. Mamitsuka is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Protein-protein interactions play a number of central roles in many cellular functions, including DNA replication, transcription and translation, signal transduction, and metabolic pathways. A recent increase in the number of protein-protein interactions has made predicting unknown protein-protein interactions important for the understanding of living cells. However, the protein-protein interactions experimentally obtained so far are often incomplete and contradictory and, consequently, existing computational prediction methods have integrated evidence (latent knowledge of proteins) from different and more reliable sources. Analyzing the relationships between proteins and the latent knowledge is important to understanding the cellular processes. For this analysis, we propose a new probabilistic model for protein-protein interactions by considering the latent knowledge of proteins. We further present an efficient learning algorithm for this model, based on an EM algorithm. Experimental results have shown that in a supervised test setting, the proposed method outperformed five other competing methods by a statistically significant factor in all cases. Using the probability parameters

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Author

H. Mamitsuka

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2005

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)