The nature of statistical learning theory
The nature of statistical learning theory
A Hierarchical Latent Variable Model for Data Visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Mining from protein–protein interactions
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Predicting functional protein-protein interactions based on computational methods
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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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 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.