{"id":"https://openalex.org/W3165308279","doi":"https://doi.org/10.1093/bib/bbab217","title":"Corrigendum to: Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data","display_name":"Corrigendum to: Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data","publication_year":2021,"publication_date":"2021-05-29","ids":{"openalex":"https://openalex.org/W3165308279","doi":"https://doi.org/10.1093/bib/bbab217","mag":"3165308279","pmid":"https://pubmed.ncbi.nlm.nih.gov/34050369"},"language":"en","primary_location":{"id":"doi:10.1093/bib/bbab217","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bib/bbab217","pdf_url":"https://academic.oup.com/bib/article-pdf/22/6/bbab217/41087924/bbab217.pdf","source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},"type":"erratum","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://academic.oup.com/bib/article-pdf/22/6/bbab217/41087924/bbab217.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008892432","display_name":"Yixuan Huang","orcid":"https://orcid.org/0000-0003-1679-8435"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixuan Huang","raw_affiliation_strings":["George Washington University School of Business, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Washington University School of Business, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100364154","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0002-6218-1885"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["Division of Immunotherapy and the Director of Bioinformatics Core at the Institute of Human Virology, University of Maryland School of Medicine, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-6218-1885","affiliations":[{"raw_affiliation_string":"Division of Immunotherapy and the Director of Bioinformatics Core at the Institute of Human Virology, University of Maryland School of Medicine, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100364154"],"corresponding_institution_ids":["https://openalex.org/I126744593"],"apc_list":{"value":4011,"currency":"USD","value_usd":4011},"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":"6","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.7430999875068665,"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/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.7430999875068665,"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/identification","display_name":"Identification (biology)","score":0.7942235469818115},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.676272988319397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4663412272930145},{"id":"https://openalex.org/keywords/transcriptome","display_name":"Transcriptome","score":0.4559226334095001},{"id":"https://openalex.org/keywords/cell-type","display_name":"Cell type","score":0.4531573951244354},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.41558241844177246},{"id":"https://openalex.org/keywords/cell","display_name":"Cell","score":0.40740546584129333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3466227352619171},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.13971108198165894},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.06813549995422363},{"id":"https://openalex.org/keywords/gene-expression","display_name":"Gene expression","score":0.045428335666656494},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.041731059551239014}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.7942235469818115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.676272988319397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4663412272930145},{"id":"https://openalex.org/C162317418","wikidata":"https://www.wikidata.org/wiki/Q252857","display_name":"Transcriptome","level":4,"score":0.4559226334095001},{"id":"https://openalex.org/C189014844","wikidata":"https://www.wikidata.org/wiki/Q189118","display_name":"Cell type","level":3,"score":0.4531573951244354},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.41558241844177246},{"id":"https://openalex.org/C1491633281","wikidata":"https://www.wikidata.org/wiki/Q7868","display_name":"Cell","level":2,"score":0.40740546584129333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3466227352619171},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.13971108198165894},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.06813549995422363},{"id":"https://openalex.org/C150194340","wikidata":"https://www.wikidata.org/wiki/Q26972","display_name":"Gene expression","level":3,"score":0.045428335666656494},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.041731059551239014},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1093/bib/bbab217","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bib/bbab217","pdf_url":"https://academic.oup.com/bib/article-pdf/22/6/bbab217/41087924/bbab217.pdf","source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},{"id":"pmid:34050369","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34050369","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":"Briefings in bioinformatics","raw_type":null}],"best_oa_location":{"id":"doi:10.1093/bib/bbab217","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bib/bbab217","pdf_url":"https://academic.oup.com/bib/article-pdf/22/6/bbab217/41087924/bbab217.pdf","source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3165308279.pdf","grobid_xml":"https://content.openalex.org/works/W3165308279.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3008241868","https://openalex.org/W3040016123","https://openalex.org/W2395369011","https://openalex.org/W4317802300","https://openalex.org/W3169186547","https://openalex.org/W4307993920","https://openalex.org/W1898241694","https://openalex.org/W2036616013","https://openalex.org/W3157307916","https://openalex.org/W2141091415"],"abstract_inverted_index":{"The":[0,287,442,505,885,904,940,1219,1284,1386,1427],"authors":[1],"regret":[2],"that":[3,11,131,423,597,1394,1480,1499,1558,1578],"the":[4,13,34,45,61,119,145,180,209,241,261,321,329,338,354,357,361,369,374,382,445,468,480,561,573,577,580,602,630,642,646,663,673,681,687,695,712,715,718,733,766,799,822,826,832,910,946,956,961,966,969,976,980,983,987,995,1002,1007,1010,1013,1016,1030,1035,1039,1052,1059,1066,1070,1086,1094,1099,1115,1126,1129,1138,1142,1150,1178,1187,1205,1214,1230,1236,1242,1247,1251,1267,1297,1304,1327,1336,1343,1373,1379,1383,1414,1445,1536,1544,1570],"published":[5],"work":[6,380,1512],"cited":[7],"in":[8,30,56,91,98,112,326,337,467,489,534,547,560,569,644,665,679,694,703,889,901,952,992,1021,1125,1177,1256,1296,1356,1423,1486,1506,1564],"reference":[9,256,365,516,1197,1319,1357,1407,1424,1450,1538],"14":[10],"benchmarked":[12,412,586,1266],"cell":[14,68,81,142,160,198,245,285,349,366,383,421,595,882,899,950,999,1019,1043,1063,1105,1123,1160,1268,1354,1380,1396,1421,1439,1459,1493,1502,1550,1572,1581],"type":[15,350,384,1044,1269,1355,1361,1397,1422,1494,1573],"prediction":[16,404,631,829,1071,1299],"methods":[17,102,239,279,331,386,446,559,1482,1521,1560],"based":[18,155,387,449,600,1278,1404],"on":[19,63,125,156,381,388,450,601,960,1069,1085,1136,1213,1279,1335,1378,1405,1444],"machine":[20,299,323,390,415,589,636,655,691,759,827,859,925,1074,1155,1208,1274,1308,1330,1434],"learning":[21,247,300,324,391,416,590,637,656,692,1075,1156,1275,1392,1435],"models":[22,296,352,417,591,693,861,927,1131,1157,1211,1228,1253,1276,1333],"for":[23,79,128,140,214,393,639,658,690,770,782,836,846,857,871,912,923,937,964,1158,1198,1225,1229,1320],"scRNA-seq":[24,237,294,613,748,1281,1509],"datasets":[25,434,503,546,614,749,767,779,815,833,1068,1282],"was":[26,447,567,649,701,862,928,958,1223],"not":[27,154,163,1504],"properly":[28],"acknowledged":[29],"this":[31,1047,1146,1263],"work.":[32],"In":[33,144,258,538,717,968,1046,1141],"Abstract":[35],"section,":[36,147,722,971,1144],"\u201cSingle-cell":[37,85],"transcriptomics":[38,86,106],"is":[39,60,71,122,132,273,508,520,667,979,1082,1388,1453,1530],"rapidly":[40],"advancing":[41,92],"our":[42,93],"understanding":[43,94],"of":[44,48,95,159,182,197,211,225,231,243,264,289,293,320,356,373,377,435,444,465,579,708,714,725,746,789,812,825,842,853,897,919,948,982,989,994,1012,1018,1034,1041,1054,1065,1073,1079,1103,1110,1119,1152,1175,1186,1272,1294,1307,1362,1399,1441,1447,1519],"cellular":[46,96],"composition":[47],"complex":[49,99],"tissues":[50],"and":[51,74,118,135,169,184,189,206,228,268,340,363,440,454,471,498,511,552,564,576,605,621,651,670,683,698,711,720,742,756,777,808,840,880,906,955,1005,1015,1032,1181,1238,1301,1348,1372,1419,1522,1553],"organisms.":[52],"A":[53],"major":[54,120,973],"limitation":[55,121],"most":[57,113,540,675,1206,1328],"analysis":[58,114,1488,1566],"pipelines":[59],"reliance":[62,124],"manual":[64,126,149,195,212],"annotations":[65,127,196],"to":[66,103,260,280,284,307,401,418,461,472,522,531,571,592,628,705,754,820,893,944,1009,1171,1182,1203,1235,1290,1302,1325,1437,1515],"determine":[67],"identities,":[69,422],"which":[70,151,308,402,490,526,645,666,764,1089],"time-consuming,":[72,133],"irreproducible,":[73,134],"sometimes":[75,136],"lack":[76,137,288],"canonical":[77,138],"markers":[78,139],"certain":[80,141],"types\u201d":[82,143],"should":[83,192,344,582,785,915,1024,1107,1260,1410,1527],"read:":[84,193,345,583,786,916,1025,1108,1261,1411,1528],"technologies":[87],"have":[88,1028,1556],"vast":[89],"potential":[90],"heterogeneity":[97],"tissues.":[100],"While":[101],"interpret":[104],"single-cell":[105,431],"data":[107,253,395,619,1055,1358,1402,1408,1425],"are":[108,152,162,1430,1483,1561],"developing":[109],"rapidly,":[110],"challenges":[111,1176,1295],"pipeline":[115],"still":[116,1454,1531],"remain,":[117],"a":[123,255,290,317,515,524,774,890,1022,1196,1318,1366,1400,1406,1448,1507],"cell-type":[129,215,282,1471,1542],"identification":[130,216,351,1161],"Introduction":[146],"\u201cThe":[148,194,851,917,1077],"annotations,":[150],"often":[153],"standardized":[157,204],"ontologies":[158,205],"labels,":[161],"reproducible":[164,190],"across":[165,170,297,502,544,557,874,1062],"different":[166,298,389,436,463,558,616,661,706,1120,1154,1173,1280,1292],"experiments":[167,460,856,922,1170],"within":[168,495,732,798],"research":[171],"groups.":[172],"These":[173],"caveats":[174],"become":[175],"even":[176],"more":[177,509,512,668],"pronounced":[178],"as":[179,306,368,400,1195,1317,1477,1543],"number":[181,224,376,1017],"cells":[183,358,533,731,797,1347,1418],"samples":[185],"increases,":[186],"preventing":[187],"fast":[188],"annotations\u201d":[191],"types":[199,1503],"usually":[200,1364],"suffer":[201],"from":[202,250,763],"limited":[203,375],"reproducibility.":[207],"And":[208],"replicability":[210],"strategies":[213],"could":[217,406,1037,1192,1314,1375],"be":[218,529,1091,1193,1315,1377,1464],"further":[219],"reduced":[220],"with":[221,328,615,1246,1369,1390,1491,1540,1569],"an":[222,1455,1532],"increased":[223],"cells,":[226,738,804,879],"clusters,":[227],"multiple":[229],"selections":[230],"cluster":[232,951,1384,1460],"marker":[233,1474,1495,1574],"genes\u201d":[234],"\u201cMachine":[235,346],"learning-based":[236,347,760,828,860,926,1209,1309,1331],"classification":[238,295,309,385,452,469,481,565,603,696,761,823,848,855,921,957,977,1130,1179,1227,1270],"predict":[240,353,1395,1438],"identity":[242],"each":[244,496,635,654,858,875,898,902,924,949,953,990,1104],"by":[246,359,426,608,864,930,1058,1093,1162,1432],"these":[248,1468],"identities":[249,596],"annotated":[251,1541],"training":[252,370,905,1545],"(e.g.,":[254,518],"atlas).":[257],"contrast":[259],"extensive":[262],"evaluations":[263],"clustering,":[265],"differential":[266],"expression":[267,1101,1117,1352,1473],"trajectory":[269],"inference":[270],"methods,":[271,762],"there":[272],"currently":[274],"one":[275,1349],"single":[276],"attempt":[277],"comparing":[278],"assign":[281,420,594],"labels":[283,1398],"clusters.":[286],"comprehensive":[291,378,1470,1537],"comparison":[292,327,507],"algorithms":[301,392],"leaves":[302,396],"users":[303,397],"without":[304,398],"indications":[305,399],"method":[310],"best":[311],"fits":[312],"their":[313,451,1257],"problem.":[314],"More":[315],"importantly,":[316],"proper":[318],"assessment":[319],"classic":[322,414,588,1273],"approaches":[325,1436],"baseline":[330],"can":[332,527,1090],"greatly":[333],"benefit":[334],"new":[335,535,1401],"developments":[336],"field":[339],"prevent":[341],"unnecessary":[342],"complexity\u201d":[343],"scRNAseq":[348,394,791,1165],"phenotype":[355],"using":[360,427,609,773,1163,1467],"large":[362,555],"well-annotated":[364,1449],"atlas":[367,1539],"sets.":[371,1359,1426],"Because":[372],"benchmarking":[379,634,653,1067],"automated":[403],"model":[405,638,657,1368],"achieve":[407],"optimal":[408],"performance[14]\u201d":[409],"\u201cHere,":[410,584],"we":[411,491,585,1049,1148,1265,1478],"10":[413,587,869,935,1153],"automatically":[419,593],"were":[424,598,626,752,768,780,818,834,844,887,909,942,1288],"evaluated":[425,448,479,599,863,929,1149],"20":[428,610,747,813,1164],"publicly":[429,611],"available":[430],"RNA":[432],"sequencing":[433],"sizes,":[437,620],"technologies,":[438,617],"species,":[439,618],"complexity.":[441,622],"performance":[443,482,566,697,824,852,918,978,1011,1040,1078,1135,1151,1222,1271,1462],"accuracy":[453,604,1072],"computation":[455,562,606,699,1258],"time.":[456],"We":[457,478,985,1167],"performed":[458,959,1168],"several":[459,1169,1554],"cover":[462,1172,1291],"levels":[464,1174,1293],"challenge":[466],"task":[470,1180],"test":[473,1183,1303],"specific":[474,1184,1305],"features":[475,575,710],"or":[476,1200,1322,1382],"tasks.":[477],"through":[483],"two":[484],"experimental":[485,549,624,1201,1286,1323],"setups:":[486],"(1)":[487,633],"intra-dataset":[488,640,682,771,837,854,920,1248],"applied":[492,530,1289],"5-fold":[493,647,866,932],"cross-validation":[494,648,775,867,933],"dataset":[497,517,792,876,991,1036,1215,1337,1443,1546],"(2)":[499,652],"inter-dataset":[500,506,659,684,783,847,1231],"involving":[501],"comparisons.":[504],"realistic":[510,669],"practical,":[513],"where":[514],"atlas)":[519],"used":[521,753,769,781,835,845,1194,1316,1490,1568],"train":[523],"classifier":[525,1210,1332],"then":[528],"identify":[532],"unannotated":[536],"datasets.":[537,1166],"general,":[539],"classifiers":[541,676,1014,1081],"perform":[542,677],"well":[543,678],"all":[545,758,765,913,965,1080,1137,1226,1520],"both":[548,680],"setups":[550],"(inter-":[551],"intra-dataset),":[553],"while":[554,686],"variation":[556],"time":[563,607,700],"observed":[568,702,1224,1479],"response":[570,704],"changing":[572],"input":[574,709],"complexity":[578,713,981,988,1008,1031,1056],"datasets\u201d":[581,716,1140],"accessible":[612],"Two":[623],"designs":[625,1287],"employed":[627,819],"measure":[629],"accuracy:":[632],"(within":[641],"dataset),":[643],"applied;":[650],"(across":[660],"datasets),":[662],"scenario":[664],"practical.":[671],"On":[672],"whole,":[674],"setups,":[685],"significant":[688],"difference":[689],"numbers":[707],"Materials":[719],"Methods":[721],"\u201cA":[723,787,972],"total":[724,788],"6":[726,790],"groups":[727,793],"(PBMC,":[728,794],"Pancreas,":[729,795],"Immune":[730,796],"tumor":[734,800,1127],"microenvironment,":[735,801,1128],"Tumor-Infiltrating":[736,802],"T":[737,803,1122],"Allen":[739,805],"Mouse":[740,806],"Brain,":[741,807],"Tabula":[743,809],"Muris)":[744,810],"consist":[745,811],"(Table":[750,816,849],"1)":[751,817],"evaluate":[755],"benchmark":[757,821,838],"evaluation":[772,1190,1312],"scheme,":[776],"16":[778,841],"evaluation\u201d":[784],"individual":[814],"models.":[830,1310],"All":[831],"design,":[839],"them":[843],"1)\u201d":[850],"applying":[865,931,1433],"(repeat":[868,934],"times":[870,936],"mean":[872,938,1100],"value)":[873],"after":[877],"genes,":[878],"small":[881],"populations":[883],"filtering.":[884],"folds":[886,908,941,963],"divided":[888],"stratified":[891,943],"manner":[892],"keep":[894],"equal":[895],"proportions":[896,947],"population":[900],"fold.":[903],"testing":[907],"same":[911,962],"classifiers\u201d":[914,967],"value).":[939],"balance":[945],"fold,":[954],"Results":[970],"aspect":[974],"affecting":[975],"dataset.":[984,1510],"defined":[986],"terms":[993],"pairwise":[996,1060,1096,1112],"similarity":[997,1061,1344,1415],"between":[998,1098,1241,1345,1416],"clusters":[1000,1020,1124],"(see":[1001],"\u201cMethods\u201d":[1003],"section)":[1004],"compared":[1006,1245],"dataset\u201d":[1023,1451],"\u201cPrevious":[1026],"studies":[1027,1555],"shown":[1029],"variability":[1033],"affect":[1038,1548],"automatic":[1042,1159,1298,1549],"classification[14].":[1045],"study,":[1048,1147,1264],"also":[1050],"investigate":[1051],"effect":[1053],"(measure":[1057],"clusters)":[1064],"models\u201d":[1076],"relatively":[1083,1220],"low":[1084],"TIT":[1087,1139],"dataset,":[1088],"explained":[1092],"high":[1095,1111],"correlations":[1097,1113],"profiles":[1102,1118],"population\u201d":[1106],"\u201cBecause":[1109],"among":[1114],"overall":[1116],"infiltrating":[1121],"show":[1132],"general":[1133],"low-level":[1134],"Discussion":[1143],"\u201cIn":[1145,1262],"aspects":[1185,1306],"classifiers.":[1188],"Our":[1189,1311],"results":[1191,1313],"bioinformatics":[1199,1321],"researchers":[1202,1324],"choose":[1204,1326],"appropriate":[1207,1329],"depending":[1212,1334],"setup":[1216,1338],"at":[1217,1339],"hand.":[1218],"lower":[1221],"experiments,":[1232],"likely":[1233],"due":[1234],"technical":[1237],"biological":[1239],"differences":[1240],"datasets,":[1243],"when":[1244,1466,1489,1567],"setup.":[1249],"Furthermore,":[1250],"tested":[1252],"vary":[1254],"considerably":[1255],"time\u201d":[1259],"(N=10)":[1277],"(N=20).":[1283],"various":[1285],"task,":[1300],"hand.\u201d":[1340],"\u201cwhich":[1341],"calculate":[1342,1413],"query":[1346,1417,1442],"representative":[1350,1420],"gene":[1351,1472,1496,1575],"per":[1353],"This":[1360],"tool":[1363],"has":[1365],"preset":[1367],"tunable":[1370],"parameters,":[1371],"neighbors":[1374],"either":[1376],"level":[1381],"level.":[1385],"other":[1387,1428],"developed":[1389,1431],"supervised":[1391],"algorithms,":[1393],"set":[1403,1475,1497,1576],"set\u201d":[1409],"\u201cthat":[1412],"tools":[1429],"identifies":[1440,1551],"basis":[1446],"\u201cIt":[1452,1529],"open":[1456,1533],"question":[1457,1534],"how":[1458,1535],"annotation":[1461],"will":[1463,1513],"affected":[1465],"eventual":[1469],"databases,":[1476],"many":[1481,1559],"highly":[1484,1562],"sensitive":[1485,1563],"precision-recall":[1487,1565],"larger":[1492,1571],"databases":[1498,1577],"contain":[1500,1579],"additional":[1501,1580],"represented":[1505],"given":[1508],"Future":[1511],"need":[1514],"study":[1516],"confusion":[1517],"matrices":[1518],"better":[1523],"quantify":[1524],"precision":[1525],"scores\u201d":[1526],"would":[1547],"prediction,":[1552],"revealed":[1557],"types[14,20]\u201d":[1582]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}