Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Spherical-harmonic decomposition for molecular recognition in electron-density maps
International Journal of Data Mining and Bioinformatics
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Guiding belief propagation using domain knowledge for protein-structure determination
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Protein X-ray crystallography -- the most popular method for determining protein structures -- remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the 3D location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.