{"id":"https://openalex.org/W3202521360","doi":null,"title":"Improved prediction of protein model quality","display_name":"Improved prediction of protein model quality","publication_year":2008,"publication_date":"2008-01-22","ids":{"openalex":"https://openalex.org/W3202521360","mag":"3202521360"},"language":"en","primary_location":{"id":"pmh:oai:conservancy.umn.edu:11299/215745","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/11299/215745","pdf_url":"http://hdl.handle.net/11299/215745","source":{"id":"https://openalex.org/S4306402521","display_name":"University of Minnesota Digital Conservancy (University of Minnesota)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I130238516","host_organization_name":"University of Minnesota","host_organization_lineage":["https://openalex.org/I130238516"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Report"},"volume":null,"type":"report","is_traditional":false,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11299/215745","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083946247","display_name":"Kevin W. DeRonne","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kevin W. DeRonne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082384108","display_name":"George Karypis","orcid":"https://orcid.org/0000-0003-2753-1437","observed_orcids":["https://orcid.org/0000-0003-2753-1437"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"George Karypis","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2753-1437","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11162","display_name":"Enzyme Structure and Function","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/protein-structure-prediction","display_name":"protein structure prediction","score":0.8420000076293945},{"id":"https://openalex.org/keywords/consensus-forecasts","display_name":"consensus forecasts","score":0.5690000057220459},{"id":"https://openalex.org/keywords/weight-learning","display_name":"weight learning","score":0.7200000286102295},{"id":"https://openalex.org/keywords/caspase-7","display_name":"caspase-7","score":0.7459999918937683}],"concepts":[{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4937821328639984},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4476965665817261},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:conservancy.umn.edu:11299/215745","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/11299/215745","pdf_url":"http://hdl.handle.net/11299/215745","source":{"id":"https://openalex.org/S4306402521","display_name":"University of Minnesota Digital Conservancy (University of Minnesota)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I130238516","host_organization_name":"University of Minnesota","host_organization_lineage":["https://openalex.org/I130238516"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Report"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.226.3617","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.226.3617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dtc.umn.edu/publications/reports/2008_01.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.401.9272","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.401.9272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.umn.edu/tech_reports_upload/tr2008/08-002.pdf","raw_type":"text"},{"id":"mag:3202521360","doi":null,"is_oa":false,"landing_page_url":"https://conservancy.umn.edu/handle/11299/215745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:conservancy.umn.edu:11299/215745","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/11299/215745","pdf_url":"http://hdl.handle.net/11299/215745","source":{"id":"https://openalex.org/S4306402521","display_name":"University of Minnesota Digital Conservancy (University of Minnesota)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I130238516","host_organization_name":"University of Minnesota","host_organization_lineage":["https://openalex.org/I130238516"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Report"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3331717440","display_name":"SEI: Virtual Screening Algorithms for Bioactive Compounds Based on Frequent Substructures","funder_award_id":"0431135","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4968282825","display_name":"CAREER: Scalable Algorithms for Knowledge Discovery in Scientific Data Sets","funder_award_id":"0133464","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309636","display_name":"University of Minnesota","ror":"https://ror.org/03grvy078"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3202521360.pdf","grobid_xml":"https://content.openalex.org/works/W3202521360.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1576520375","https://openalex.org/W1604938182","https://openalex.org/W1965517743","https://openalex.org/W1996283866","https://openalex.org/W1997637252","https://openalex.org/W2005049999","https://openalex.org/W2011345135","https://openalex.org/W2032316736","https://openalex.org/W2040870580","https://openalex.org/W2042281163","https://openalex.org/W2048290645","https://openalex.org/W2097874946","https://openalex.org/W2099667027","https://openalex.org/W2110483430","https://openalex.org/W2118836759","https://openalex.org/W2142013909","https://openalex.org/W2142144955","https://openalex.org/W2146983134","https://openalex.org/W2153187042","https://openalex.org/W2156909104"],"related_works":["https://openalex.org/W2025469940","https://openalex.org/W2133825909","https://openalex.org/W2090778977","https://openalex.org/W2125165264","https://openalex.org/W3198485384","https://openalex.org/W2103986334","https://openalex.org/W2095415034","https://openalex.org/W2104879218","https://openalex.org/W3016273480","https://openalex.org/W2008202581"],"abstract_inverted_index":{"Background:":[0],"Methods":[1],"that":[2,32,57,168,219,243],"can":[3,265,268],"automatically":[4],"assess":[5],"the":[6,18,21,34,44,60,64,74,91,96,99,108,113,144,148,177,181,187,220,231,251,259,262],"quality":[7,35,75,179],"of":[8,20,28,36,63,76,137,180,189,222,227,255,261],"computationally":[9],"predicted":[10,47,78,102,178],"protein":[11,79,101],"structures":[12,46,97,260],"are":[13],"important,":[14],"as":[15],"they":[16],"enable":[17],"selection":[19],"most":[22],"accurate":[23],"structure":[24,39,65,80,92],"from":[25,124,250],"an":[26,70,195],"ensemble":[27],"predictions.":[29],"Assessment":[30],"methods":[31],"determine":[33],"a":[37,77,82,119,131,166,202,208,212,245,253],"protein\u2019s":[38],"by":[40,48,103,111,147,164,257],"comparing":[41],"it":[42],"against":[43],"various":[45],"different":[49,104,263],"servers":[50,105,264],"have":[51],"been":[52],"shown":[53],"to":[54,72,89,95,162,173,193,201],"outperform":[55],"approaches":[56,192],"rely":[58],"on":[59,176,211],"intrinsic":[61],"characteristics":[62],"itself.":[66],"Results:":[67],"We":[68,157,184],"developed":[69],"algorithm":[71],"estimate":[73],"using":[81],"consensus":[83],"approach.":[84],"Our":[85,216],"method":[86,129],"uses":[87],"LGA":[88,223],"align":[90],"in":[93,151],"question":[94],"for":[98,153,234],"same":[100],"and":[106,225],"estimates":[107],"per-residue":[109],"error":[110,135],"averaging":[112],"distances":[114,172,229],"across":[115],"these":[116,160],"alignments.":[117],"On":[118],"dataset":[120],"containing":[121],"892,299":[122],"positions":[123],"4,969":[125],"CASP7":[126,152],"submissions,":[127],"our":[128,240],"achieved":[130,146],"root":[132],"mean":[133],"squared":[134],"(RMSE)":[136],"6.69\u02daA,":[138],"which":[139,171,199,258],"is":[140,230],"significantly":[141],"better":[142],"than":[143],"8.21\u02daA":[145],"winning":[149],"scheme":[150,167],"this":[154],"problem":[155],"(Pcons).":[156],"further":[158,269],"improved":[159],"results":[161,217,241],"6.51\u02daA":[163],"developing":[165],"carefully":[169],"selects":[170],"average":[174],"based":[175],"overall":[182],"structure.":[183],"also":[185],"examined":[186],"use":[188,221],"machine":[190],"learning":[191,205,249],"learn":[194],"appropriate":[196],"aggregation":[197,226],"scheme,":[198],"led":[200],"simple":[203],"weight":[204],"approach":[206],"achieving":[207],"2.61\u02daA":[209],"RMSE":[210],"reduced":[213],"dataset.":[214],"Conclusions:":[215],"show":[218,242],"alignments":[224],"raw":[228],"primary":[232],"reason":[233],"its":[235],"performance":[236],"advantage.":[237],"In":[238],"addition,":[239],"beyond":[244],"binary":[246],"inclusion/exclusion":[247],"decision,":[248],"data":[252],"set":[254],"weights":[256],"be":[266],"aggregated":[267],"improve":[270],"performance.":[271],"1":[272]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2021-10-11T00:00:00"}
