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Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach

Author:

Hiroshi MamitsukaAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 2, Issue 2

Pages 119 - 130

https://doi.org/10.1109/TCBB.2005.23

Published: 01 April 2005 Publication History

Abstract

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 of a trained model, we have further shown the latent knowledge that is essential to predicting protein-protein interactions. Overall, our experimental results confirm that our proposed model is especially effective for analyzing protein-protein interactions from a viewpoint of the latent knowledge of proteins.

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Cited By

Mamitsuka H(2012)Mining from protein–protein interactionsWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.10652:5(400-410)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1002/widm.1065
Zhang LZhang W(2007)Predicting functional protein-protein interactions based on computational methodsProceedings of the 2007 international conference on Life System Modeling and Simulation10.5555/2393672.2393717(354-363)Online publication date: 14-Sep-2007
https://dl.acm.org/doi/10.5555/2393672.2393717

Index Terms

Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 2, Issue 2

April 2005

95 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 April 2005

Published in TCBB Volume 2, Issue 2

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Cited By

Mamitsuka H(2012)Mining from protein–protein interactionsWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.10652:5(400-410)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1002/widm.1065
Zhang LZhang W(2007)Predicting functional protein-protein interactions based on computational methodsProceedings of the 2007 international conference on Life System Modeling and Simulation10.5555/2393672.2393717(354-363)Online publication date: 14-Sep-2007
https://dl.acm.org/doi/10.5555/2393672.2393717

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