{"id":"https://openalex.org/W2113381588","doi":"https://doi.org/10.1186/1471-2105-13-s16-s2","title":"False discovery rates in spectral identification","display_name":"False discovery rates in spectral identification","publication_year":2012,"publication_date":"2012-11-01","ids":{"openalex":"https://openalex.org/W2113381588","doi":"https://doi.org/10.1186/1471-2105-13-s16-s2","mag":"2113381588","pmid":"https://pubmed.ncbi.nlm.nih.gov/23176207"},"language":"en","primary_location":{"id":"doi:10.1186/1471-2105-13-s16-s2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/1471-2105-13-s16-s2","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-13-S16-S2","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-13-S16-S2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033090793","display_name":"Kyowon Jeong","orcid":"https://orcid.org/0000-0003-3776-3098"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kyowon Jeong","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101480533","display_name":"Sangtae Kim","orcid":"https://orcid.org/0000-0001-7567-3814"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sangtae Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087034314","display_name":"Nuno Bandeira","orcid":"https://orcid.org/0000-0001-8385-3655"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nuno Bandeira","raw_affiliation_strings":["Center for Computational Mass Spectrometry, University of California San Diego, San Diego, CA, USA","Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA","Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"Center for Computational Mass Spectrometry, University of California San Diego, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033090793"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":{"value":1690,"currency":"GBP","value_usd":2072},"apc_paid":{"value":1690,"currency":"GBP","value_usd":2072},"fwci":6.1198,"has_fulltext":true,"cited_by_count":168,"citation_normalized_percentile":{"value":0.97498878,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"13","issue":"S16","first_page":"S2","last_page":"S2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10519","display_name":"Advanced Proteomics Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10519","display_name":"Advanced Proteomics Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10683","display_name":"Mass Spectrometry Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9940000176429749,"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/false-discovery-rate","display_name":"False discovery rate","score":0.9240955114364624},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.8359405994415283},{"id":"https://openalex.org/keywords/false-positives-and-false-negatives","display_name":"False positives and false negatives","score":0.6517532467842102},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5834619998931885},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5737712383270264},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5622356534004211},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5011446475982666},{"id":"https://openalex.org/keywords/multiple-comparisons-problem","display_name":"Multiple comparisons problem","score":0.45526623725891113},{"id":"https://openalex.org/keywords/mascot","display_name":"Mascot","score":0.446547269821167},{"id":"https://openalex.org/keywords/false-positive-rate","display_name":"False positive rate","score":0.4286336898803711},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4130488932132721},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.3463301360607147},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21719351410865784},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21064388751983643},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20116174221038818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19631773233413696},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.1394599974155426}],"concepts":[{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.9240955114364624},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.8359405994415283},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.6517532467842102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5834619998931885},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5737712383270264},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5622356534004211},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5011446475982666},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.45526623725891113},{"id":"https://openalex.org/C2778160393","wikidata":"https://www.wikidata.org/wiki/Q18670161","display_name":"Mascot","level":2,"score":0.446547269821167},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.4286336898803711},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4130488932132721},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.3463301360607147},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21719351410865784},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21064388751983643},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20116174221038818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19631773233413696},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.1394599974155426},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D011506","descriptor_name":"Proteins","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D011506","descriptor_name":"Proteins","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D011506","descriptor_name":"Proteins","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D030562","descriptor_name":"Databases, Protein","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D030562","descriptor_name":"Databases, Protein","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D030562","descriptor_name":"Databases, Protein","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D053719","descriptor_name":"Tandem Mass Spectrometry","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D053719","descriptor_name":"Tandem Mass Spectrometry","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D053719","descriptor_name":"Tandem Mass Spectrometry","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false},{"descriptor_ui":"D057386","descriptor_name":"Search Engine","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D057386","descriptor_name":"Search Engine","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D057386","descriptor_name":"Search Engine","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.1186/1471-2105-13-s16-s2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/1471-2105-13-s16-s2","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-13-S16-S2","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},{"id":"pmid:23176207","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/23176207","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC bioinformatics","raw_type":null},{"id":"pmh:oai:doaj.org/article:d5cc6c2ea46e474b810db12dfa16e7cf","is_oa":true,"landing_page_url":"https://doaj.org/article/d5cc6c2ea46e474b810db12dfa16e7cf","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics, Vol 13, Iss Suppl 16, p S2 (2012)","raw_type":"article"},{"id":"pmh:oai:escholarship.org/ark:/13030/qt168143bk","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/168143bk","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics, vol 13, iss Suppl 16","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:3489529","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3489529","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics","raw_type":"Text"},{"id":"pmh:qt168143bk","is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/168143bk","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jeong, Kyowon; Kim, Sangtae; & Bandeira, Nuno. (2012). False discovery rates in spectral identification. BMC Bioinformatics, 13(Suppl 16), S2. doi: http://dx.doi.org/10.1186/1471-2105-13-S16-S2. Retrieved from: http://www.escholarship.org/uc/item/168143bk","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/1471-2105-13-s16-s2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/1471-2105-13-s16-s2","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-13-S16-S2","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337375","display_name":"National Center for Research Resources","ror":"https://ror.org/04pw6fb54"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2113381588.pdf","grobid_xml":"https://content.openalex.org/works/W2113381588.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1418224","https://openalex.org/W1981593008","https://openalex.org/W1984566266","https://openalex.org/W1999751815","https://openalex.org/W2005231034","https://openalex.org/W2013589890","https://openalex.org/W2026465178","https://openalex.org/W2027927304","https://openalex.org/W2030578978","https://openalex.org/W2043828494","https://openalex.org/W2044377737","https://openalex.org/W2047275456","https://openalex.org/W2049293796","https://openalex.org/W2058015510","https://openalex.org/W2075036506","https://openalex.org/W2078931204","https://openalex.org/W2079752406","https://openalex.org/W2090647436","https://openalex.org/W2096057003","https://openalex.org/W2105381419","https://openalex.org/W2105889769","https://openalex.org/W2112078820","https://openalex.org/W2130706354","https://openalex.org/W2133172947","https://openalex.org/W2135337307","https://openalex.org/W2147698768","https://openalex.org/W2163078532","https://openalex.org/W2290195878"],"related_works":["https://openalex.org/W2020413892","https://openalex.org/W1991225657","https://openalex.org/W2389124870","https://openalex.org/W4388328864","https://openalex.org/W2168149717","https://openalex.org/W2079592427","https://openalex.org/W2317297514","https://openalex.org/W2093463665","https://openalex.org/W4212966421","https://openalex.org/W2102122784"],"abstract_inverted_index":{"Automated":[0],"database":[1,233,276,306],"search":[2,234,270,277],"engines":[3,9],"are":[4,172],"one":[5],"of":[6,10,16,18,20,44,82,95,102,105,123,160,165,168,184,193,196,199,217,225,232,241,260,285,295,301,314],"the":[7,41,76,83,92,100,121,157,163,166,182,191,194,226,239,268,273,283,299],"fundamental":[8,93],"high-throughput":[11,49],"proteomics":[12],"enabling":[13],"daily":[14],"identifications":[15,46,85,139,169,243,303],"hundreds":[17],"thousands":[19],"peptides":[21],"and":[22,170,252,310],"proteins":[23],"from":[24,47,205],"tandem":[25],"mass":[26],"(MS/MS)":[27],"spectrometry":[28],"data.":[29],"Nevertheless,":[30],"this":[31],"automation":[32],"also":[33],"makes":[34],"it":[35],"humanly":[36],"impossible":[37],"to":[38,62,119,135,149,211],"manually":[39],"validate":[40],"vast":[42],"lists":[43,104],"resulting":[45,302],"such":[48],"searches.":[50,261],"This":[51],"challenge":[52],"is":[53,108,145,162],"usually":[54],"addressed":[55],"by":[56,282],"using":[57],"a":[58,71,213,230,293],"Target-Decoy":[59],"Approach":[60],"(TDA)":[61],"impose":[63],"an":[64],"empirical":[65,200,315],"False":[66],"Discovery":[67],"Rate":[68],"(FDR)":[69],"at":[70,79],"pre-determined":[72],"threshold":[73],"x%":[74,81],"with":[75,308],"expectation":[77],"that":[78,238,267],"most":[80,274],"returned":[84],"would":[86],"be":[87,117,246,254],"false":[88,186,242],"positives.":[89,187],"But":[90],"despite":[91],"importance":[94],"FDR":[96,125,215,219],"estimates":[97,133,198],"in":[98,137,154,229],"ensuring":[99],"utility":[101],"large":[103],"identifications,":[106],"there":[107,144,171],"surprisingly":[109],"little":[110,147],"consensus":[111],"on":[112,181],"exactly":[113],"how":[114],"TDA":[115,197,227,312],"should":[116],"applied":[118],"minimize":[120],"chances":[122],"biased":[124],"estimates.":[126],"In":[127,262],"fact,":[128],"since":[129],"less":[130],"rigorous":[131],"TDA/FDR":[132,152],"tend":[134],"result":[136],"more":[138],"(at":[140],"higher":[141,249],"'true'":[142,218],"FDR),":[143],"often":[146],"incentive":[148],"enforce":[150],"strict":[151],"procedures":[153],"studies":[155,176],"where":[156,207],"major":[158],"metric":[159],"success":[161],"size":[164],"list":[167],"no":[173],"follow":[174],"up":[175],"imposing":[177],"hard":[178],"cost":[179],"constraints":[180],"number":[183,300],"reported":[185,251],"Here":[188],"we":[189,208,220,264],"address":[190],"problem":[192],"accuracy":[195],"FDR.":[201,316],"Using":[202],"MS/MS":[203],"spectra":[204],"samples":[206],"were":[209],"able":[210],"define":[212],"factual":[214],"estimator":[216],"evaluate":[221],"several":[222],"popular":[223],"variants":[224],"procedure":[228],"variety":[231],"contexts.":[235],"We":[236],"show":[237],"fraction":[240],"can":[244],"sometimes":[245],"over":[247],"10\u00d7":[248],"than":[250],"may":[253],"unavoidably":[255,280],"high":[256],"for":[257],"certain":[258],"types":[259],"addition,":[263],"further":[265],"report":[266],"two-pass":[269],"strategy":[271],"seems":[272],"promising":[275],"strategy.":[278],"While":[279],"constrained":[281],"particulars":[284],"any":[286],"specific":[287],"evaluation":[288],"dataset,":[289],"our":[290],"observations":[291],"support":[292],"series":[294],"recommendations":[296],"towards":[297],"maximizing":[298],"while":[304],"controlling":[305],"searches":[307],"robust":[309],"reproducible":[311],"estimation":[313]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":15},{"year":2015,"cited_by_count":19},{"year":2014,"cited_by_count":15},{"year":2013,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}