<HashMap><database>biostudies-literature</database><scores><citationCount>0</citationCount><reanalysisCount>0</reanalysisCount><viewCount>47</viewCount><searchCount>0</searchCount></scores><additional><submitter>Cowtan K</submitter><funding>Biotechnology and Biological Sciences Research Council</funding><pagination>470-8</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC2852311</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>66(Pt 4)</volume><pubmed_abstract>Classical density-modification techniques (as opposed to statistical approaches) offer a computationally cheap method for improving phase estimates in order to provide a good electron-density map for model building. The rise of statistical methods has lead to a shift in focus away from the classical approaches; as a result, some recent developments have not made their way into classical density-modification software. This paper describes the application of some recent techniques, including most importantly the use of prior phase information in the likelihood estimation of phase errors within a classical density-modification framework. The resulting software gives significantly better results than comparable classical methods, while remaining nearly two orders of magnitude faster than statistical methods.</pubmed_abstract><journal>Acta crystallographica. Section D, Biological crystallography</journal><pubmed_title>Recent developments in classical density modification.</pubmed_title><pmcid>PMC2852311</pmcid><funding_grant_id>BB/D522403/1</funding_grant_id><pubmed_authors>Cowtan K</pubmed_authors><view_count>47</view_count></additional><is_claimable>false</is_claimable><name>Recent developments in classical density modification.</name><description>Classical density-modification techniques (as opposed to statistical approaches) offer a computationally cheap method for improving phase estimates in order to provide a good electron-density map for model building. The rise of statistical methods has lead to a shift in focus away from the classical approaches; as a result, some recent developments have not made their way into classical density-modification software. This paper describes the application of some recent techniques, including most importantly the use of prior phase information in the likelihood estimation of phase errors within a classical density-modification framework. The resulting software gives significantly better results than comparable classical methods, while remaining nearly two orders of magnitude faster than statistical methods.</description><dates><release>2010-01-01T00:00:00Z</release><publication>2010 Apr</publication><modification>2021-02-20T23:56:38Z</modification><creation>2019-03-27T00:00:46Z</creation></dates><accession>S-EPMC2852311</accession><cross_references><pubmed>20383000</pubmed><doi>10.1107/S090744490903947X</doi></cross_references></HashMap>