Predicting protein-protein relationships from literature using latent topics.
This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it has not been investigated for such a task. In this paper, we apply the state-of-the-art Collapsed Variational Bayesian Inference and Gibbs Sampling inference to estimating the LDA model. We also apply probabilistic Latent Semantic Analysis (pLSA) as a baseline for comparison, and compare them from the viewpoints of log-likelihood, classification accuracy and retrieval effectiveness. We demonstrate through experiments that the Collapsed Variational LDA gives better results than the others, especially in terms of classification accuracy and retrieval effectiveness in the task of the protein-protein relationship prediction.
Aso
Tatsuya
T
Department of Computer Science and Systems Engineering, Kobe University, 1-1 Rokkoudai, Nada-ku, Kobe 657-8501, Japan. dango-r@cs25.scitec.kobe-u.ac.jp
Eguchi
Koji
K
D000465:Algorithms; D001499:Bayes Theorem; D016013:Likelihood Functions; D011336:Probability; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D011506:Proteins