{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T03:01:44Z","timestamp":1649127704899},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2012,2]]},"abstract":"<jats:p> Protein X-ray crystallography \u2014 the most popular method for determining protein structures \u2014 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 three-dimensional (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. <\/jats:p>","DOI":"10.1142\/s0219720012400094","type":"journal-article","created":{"date-parts":[[2011,12,28]],"date-time":"2011-12-28T21:31:16Z","timestamp":1325107876000},"page":"1240009","source":"Crossref","is-referenced-by-count":2,"title":["PROBABILISTIC ENSEMBLES FOR IMPROVED INFERENCE IN PROTEIN-STRUCTURE DETERMINATION"],"prefix":"10.1142","volume":"10","author":[{"given":"AMEET","family":"SONI","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, Department of Biostatistics and Medical Informatics, University of Wisconsin\u2013Madison, Madison, WI 53706, USA"}]},{"given":"JUDE","family":"SHAVLIK","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Department of Biostatistics and Medical Informatics, University of Wisconsin\u2013Madison, Madison, WI 53706, USA"}]}],"member":"219","published-online":{"date-parts":[[2012,4,30]]},"reference":[{"key":"rf1","volume-title":"Probabilistic Graphical Models: Principles and Techniques \u2014 Adaptive Computation and Machine Learning","author":"Koller D.","year":"2009"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl252"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm480"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1504\/IJDMB.2009.024852"},{"key":"rf6","first-page":"721","volume":"6","author":"Geman S.","journal-title":"IEEE. Trans. Pattern. Anal. Mach. Intell."},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1038\/8263"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1016\/S0076-6879(03)74012-9"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1107\/S0907444902018036"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"},{"key":"rf13","unstructured":"D.\u00a0Weiss, B.\u00a0Sapp and B.\u00a0Taskar, Advances in Neural Information Processing Systems\u00a023, eds. J.\u00a0Lafferty (2010)\u00a0pp. 2415\u20132423."},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1109\/78.978374"}],"container-title":["Journal of Bioinformatics and Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219720012400094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T13:41:03Z","timestamp":1565098863000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219720012400094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2]]},"references-count":11,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2012,4,30]]},"published-print":{"date-parts":[[2012,2]]}},"alternative-id":["10.1142\/S0219720012400094"],"URL":"https:\/\/doi.org\/10.1142\/s0219720012400094","relation":{},"ISSN":["0219-7200","1757-6334"],"issn-type":[{"value":"0219-7200","type":"print"},{"value":"1757-6334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,2]]}}}