{"id":"https://openalex.org/W3010601552","doi":"https://doi.org/10.1002/cyto.a.23986","title":"Special Issue on Machine Learning for Single Cell Data","display_name":"Special Issue on Machine Learning for Single Cell Data","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3010601552","doi":"https://doi.org/10.1002/cyto.a.23986","mag":"3010601552","pmid":"https://pubmed.ncbi.nlm.nih.gov/32142597"},"language":"en","primary_location":{"id":"doi:10.1002/cyto.a.23986","doi":"https://doi.org/10.1002/cyto.a.23986","is_oa":true,"landing_page_url":"https://doi.org/10.1002/cyto.a.23986","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cyto.a.23986","source":{"id":"https://openalex.org/S86468533","display_name":"Cytometry Part A","issn_l":"1552-4922","issn":["1552-4922","1552-4930"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cytometry Part A","raw_type":"journal-article"},"volume":null,"type":"editorial","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cyto.a.23986","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001585713","display_name":"Yvan Saeys","orcid":"https://orcid.org/0000-0002-0415-1506","observed_orcids":["https://orcid.org/0000-0002-0415-1506"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yvan Saeys","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5069196767","display_name":"Greg Finak","orcid":"https://orcid.org/0000-0003-4341-9090","observed_orcids":["https://orcid.org/0000-0003-4341-9090"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Greg Finak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":3000,"currency":"USD","value_usd":3000},"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"97","issue":"3","first_page":"217","last_page":"218"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.9976000189781189,"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":0.9976000189781189,"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/T10885","display_name":"Gene expression and cancer classification","score":0.0006000000284984708,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/single-cell-data","display_name":"single-cell data","score":0.9319999814033508},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.8849999904632568},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.7549999952316284},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"supervised learning","score":0.9929999709129333},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8009999990463257},{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.8830000162124634},{"id":"https://openalex.org/keywords/single-cell-trajectory-inference","display_name":"single-cell trajectory analysis","score":0.9890000224113464},{"id":"https://openalex.org/keywords/cell-type-classification","display_name":"cell type classification","score":0.7829999923706055},{"id":"https://openalex.org/keywords/cell-state-classification","display_name":"cell state classification","score":0.38600000739097595},{"id":"https://openalex.org/keywords/imaging-mass-cytometry","display_name":"imaging mass cytometry","score":0.6710000038146973},{"id":"https://openalex.org/keywords/imaging-flow-cytometry","display_name":"imaging flow cytometry","score":0.8360000252723694},{"id":"https://openalex.org/keywords/spatial-omics","display_name":"spatial omics","score":0.8809999823570251},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.5809999704360962},{"id":"https://openalex.org/keywords/single-cell-sequencing","display_name":"single-cell sequencing","score":0.4490000009536743},{"id":"https://openalex.org/keywords/mass-cytometry","display_name":"mass cytometry","score":0.7110000252723694},{"id":"https://openalex.org/keywords/flow-cytometry","display_name":"flow cytometry","score":0.7509999871253967},{"id":"https://openalex.org/keywords/disease-prediction","display_name":"disease prediction","score":0.38100001215934753},{"id":"https://openalex.org/keywords/drug-response-prediction","display_name":"drug response prediction","score":0.7210000157356262},{"id":"https://openalex.org/keywords/patient-survival-prediction","display_name":"patient survival prediction","score":0.8009999990463257},{"id":"https://openalex.org/keywords/cell-heterogeneity","display_name":"cellular heterogeneity","score":0.3610000014305115}],"concepts":[{"id":"https://openalex.org/C2778015335","wikidata":"https://www.wikidata.org/wiki/Q17051163","display_name":"Mass cytometry","level":4,"score":0.8001738786697388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6744735836982727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6739180088043213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6264255046844482},{"id":"https://openalex.org/C2780339063","wikidata":"https://www.wikidata.org/wiki/Q5201367","display_name":"Cytometry","level":3,"score":0.5423257350921631},{"id":"https://openalex.org/C118530786","wikidata":"https://www.wikidata.org/wiki/Q1134732","display_name":"Instrumentation (computer programming)","level":2,"score":0.47768908739089966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35908734798431396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3478888273239136},{"id":"https://openalex.org/C553184892","wikidata":"https://www.wikidata.org/wiki/Q1141429","display_name":"Flow cytometry","level":2,"score":0.2772443890571594},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.11684879660606384},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","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}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":3,"locations":[{"id":"doi:10.1002/cyto.a.23986","doi":"https://doi.org/10.1002/cyto.a.23986","is_oa":true,"landing_page_url":"https://doi.org/10.1002/cyto.a.23986","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cyto.a.23986","source":{"id":"https://openalex.org/S86468533","display_name":"Cytometry Part A","issn_l":"1552-4922","issn":["1552-4922","1552-4930"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cytometry Part A","raw_type":"journal-article"},{"id":"pmid:32142597","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32142597","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,"listed_in":[],"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":"Cytometry. Part A : the journal of the International Society for Analytical Cytology","raw_type":"Editorial"},{"id":"pmh:oai:archive.ugent.be:8661076","doi":null,"is_oa":false,"landing_page_url":"https://biblio.ugent.be/publication/8661076","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1552-4930","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1002/cyto.a.23986","doi":"https://doi.org/10.1002/cyto.a.23986","is_oa":true,"landing_page_url":"https://doi.org/10.1002/cyto.a.23986","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cyto.a.23986","source":{"id":"https://openalex.org/S86468533","display_name":"Cytometry Part A","issn_l":"1552-4922","issn":["1552-4922","1552-4930"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cytometry Part A","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3010601552.pdf","grobid_xml":"https://content.openalex.org/works/W3010601552.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2509590826","https://openalex.org/W4391262644","https://openalex.org/W7125808678","https://openalex.org/W3169674085","https://openalex.org/W3128617908","https://openalex.org/W3210186213","https://openalex.org/W4399546383","https://openalex.org/W4402149246","https://openalex.org/W2043345494","https://openalex.org/W2368109179"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,145,263,301,420,434,486,544,561,618,628,663,788,833,874,966,973,1007,1109,1123,1154,1262,1383,1466,1490,1550],"instrumentation":[3],"have":[4,74,97,142,1024,1034],"led":[5],"to":[6,54,83,103,154,178,190,215,256,328,520,579,596,600,613,621,778,829,860,870,887,934,1125,1143,1201,1252,1305,1321,1336,1377,1395,1429,1459,1499,1519,1525,1566],"a":[7,180,194,206,312,330,400,466,575,644,671,780,826,834,897,1134,1389,1415,1454,1526],"wealth":[8],"of":[9,14,59,67,79,139,209,283,288,304,314,375,391,399,409,443,457,479,529,534,539,656,660,670,674,735,765,774,783,795,803,852,882,909,932,938,984,1005,1056,1103,1137,1148,1181,1208,1280,1301,1310,1366,1391,1399,1433,1446,1463,1478,1485,1529,1547,1554,1563],"novel":[10,101,160,281,481,576,1012,1068,1135],"single-cell":[11,39,85,105,305,334,350,364,458,482,854,1018,1048,1062,1486],"technologies,":[12],"many":[13,478,734],"which":[15,300,469,736,1564],"generate":[16],"large":[17,241],"and":[18,36,94,130,150,158,205,226,246,271,276,316,336,373,387,389,413,431,463,492,511,526,567,608,639,668,695,739,743,746,754,791,958,979,1041,1079,1082,1099,1101,1114,1118,1225,1342,1345,1403,1422,1513],"high-dimensional":[19],"datasets.":[20],"Examples":[21],"include":[22,112,268],"high-parameter":[23],"flow":[24,117,911,1127,1222,1285],"cytometry":[25,31,95,118,125,664,883,912,1128,1223,1286,1385],"(Chattopadhyay":[26],"et":[27,33,43,120,127,135,166,503,507,515,571,683,687,691,699,703,707,711,715,719,723,727,731,808,812,823,902,1051,1131,1194,1291,1371,1450],"al.":[28,34,44,121,128,136,167,504,508,516,572,684,688,692,700,704,708,712,716,720,724,728,732,809,813,824,1052,1132,1195,1292,1451],"2019),":[29],"mass":[30,124,1384],"(Bandura":[32],"2009)":[35],"recently":[37,278],"also":[38,98,279,495,980,1045,1112,1218,1363,1413],"sequencing":[40],"techniques":[41,51,73,96,113,133,141,153,171,175,210,252,290,322,547,680,924,1060,1261],"(Kolodziejczyk":[42],"2015).":[45],"Depending":[46],"on":[47,323,468,647,787,818,926,1178,1220,1273,1326,1419,1534,1544],"the":[48,156,183,231,258,264,280,297,302,324,333,349,438,455,480,524,527,540,559,602,614,619,640,651,657,666,763,772,789,793,800,804,888,893,904,907,977,997,1047,1206,1216,1238,1296,1299,1307,1323,1348,1355,1364,1367,1374,1379,1397,1406,1431,1545,1552,1561],"technology,":[49],"some":[50,78,538,757,1246],"are":[52,172,236,418,474,484,494,518,878,1362,1517],"able":[53,82,869],"provide":[55,84,756,1053,1409],"measurements":[56,107,380],"for":[57,182,427,490,548,581,605,677,880,914,1016,1028,1038,1061,1205,1245,1283,1443,1474,1557],"thousands":[58],"cells,":[60],"while":[61],"others":[62],"can":[63,211,253,307,355,1231,1471],"easily":[64],"measure":[65,1145,1460],"millions":[66],"cells":[68,315,933,957,969,1324,1465,1479],"per":[69],"sample.":[70],"Also":[71],"imaging":[72,93,116,123,1105,1126,1221,1284],"been":[75,1025,1035],"rapidly":[76],"evolving,":[77],"them":[80,1254],"being":[81,186],"measurements,":[86],"e.g.":[87],"using":[88,775,996,1138,1185,1236,1255,1340],"segmentation":[89],"techniques.":[90,249,768,1189],"Hybridizations":[91],"between":[92,332],"emerged":[99],"as":[100,115,201,311,344,346,1072,1086,1173,1480,1482,1489,1510],"tools":[102,777,1508,1523],"combine":[104,919],"multi-parameter":[106],"with":[108,221,586,885,929,976,1161,1354,1504],"spatial":[109,131,1444,1461],"information.":[110,362],"These":[111],"such":[114,1071,1085,1488,1509],"(McGrath":[119],"2008),":[122],"(Giesen":[126],"2014),":[129],"\u201comics\u201d":[132],"(St\u00e5hl":[134],"2016).":[137,168,748],"All":[138,287],"these":[140,217,289,611,1166],"one":[143],"thing":[144],"common:":[146],"they":[147,228,948,1174,1317],"require":[148,291],"statistical":[149],"machine":[151,196,428,471,545,661,998,1013,1120,1198,1228,1269,1281,1439],"learning":[152,170,197,234,244,248,251,321,429,472,546,662,999,1014,1022,1032,1058,1069,1117,1121,1199,1203,1229,1260,1270,1282,1400,1440,1536],"interpret":[155,935],"data":[157,184,298,306,335,432,440,790,1487],"extract":[159],"biological":[161,359,392],"knowledge":[162],"from":[163,348,378,406,1314],"it":[164,1249],"(Saeys":[165],"Machine":[169,233],"data-driven":[173],"modeling":[174],"that":[176,260,340,437,555,632,649,847,877,965,990,1152,1244,1347,1405,1425,1457],"aim":[177,327,1376],"learn":[179,257,329,601,830,871,1378],"model":[181,1430],"without":[185],"explicitly":[187],"told":[188],"how":[189],"do":[191,1175],"so.":[192],"Typically,":[193],"specific":[195,361,653],"task":[198],"is":[199,261,436,465,556,564,631,785,841,868,1250,1287,1537,1565],"specified":[200],"an":[202,337,1054],"optimization":[203,218,851],"problem,":[204],"wide":[207,672,1527],"array":[208],"typically":[212,237],"be":[213,254,308,342,356,442,636,992,1472],"used":[214,255,1473],"solve":[216],"problems,":[219],"all":[220,1438],"their":[222,317,866,1332,1501],"own":[223],"advantages,":[224],"disadvantages":[225],"assumptions":[227],"make":[229,1500,1520],"regarding":[230],"data.":[232,265,351,551,1019,1129,1386],"tasks":[235],"divided":[238],"into":[239],"two":[240,369],"categories:":[242],"unsupervised":[243,862],"techniques,":[245,273,275,430,865,1122],"supervised":[247,835],"Unsupervised":[250],"structure":[259],"present":[262,573,1382,1414,1452],"Common":[266],"examples":[267],"dimensionality":[269],"reduction":[270],"visualization":[272,931],"clustering":[274,648,675,927],"more":[277,796,971,993,1169,1410],"class":[282,838],"trajectory":[284,1302],"inference":[285,1303,1360],"methods.":[286],"no":[292],"additional":[293],"information":[294,840,890,1240],"than":[295],"just":[296],"points,":[299],"case":[303,456,1156,1264],"usually":[309],"represented":[310],"matrix":[313],"properties":[318],"measured.":[319],"Supervised":[320],"other":[325,414,615,1119,1483,1505],"hand":[326],"function":[331],"external":[338,353],"variable":[339,354],"should":[341,441],"predicted":[343],"well":[345,1353,1481],"possible":[347,1251],"This":[352,531,1469],"cell-specific":[357],"or":[358,384,402,1493],"sample":[360],"In":[363,454,749,815,896,1164,1190,1213,1293],"analysis,":[365,1040],"we":[366],"generally":[367],"see":[368],"broad":[370],"applications:":[371],"identification":[372,388],"classification":[374,390,855,881],"cell":[376,411,550,940,952,985,1092,1094,1104,1211,1234,1247,1338,1447,1558,1571],"types":[377,412,1235,1248,1484],"cell-level":[379],"(e.g.":[381,394],"protein,":[382],"RNA,":[383],"image":[385,1029,1039,1064,1087,1090,1097,1171,1187,1328,1559],"features),":[386],"samples":[393,588,617,884],"predicting":[395],"disease":[396],"status,":[397],"dose":[398],"drug,":[401],"patient":[403,894],"survival),":[404],"often":[405],"differential":[407],"abundance":[408],"inferred":[410,1407],"features.":[415,1276,1329],"Both":[416],"applications":[417,659,1084],"presented":[419,1288],"this":[421,562,629,750,946,1008,1042,1110,1155,1214,1263,1294],"special":[422,532,1009],"issue.":[423],"An":[424,625],"important":[425,626],"prerequisite":[426],"mining":[433],"general,":[435],"input":[439],"sufficient":[444],"quality":[445,461,491,565],"(the":[446],"\u201cgarbage":[447],"in,":[448],"garbage":[449],"out\u201d":[450],"principle":[451],"always":[452],"applies).":[453],"data,":[459,1224],"good":[460,781],"control":[462,566,1102],"preprocessing":[464,493],"cornerstone":[467],"subsequent":[470,898,1003],"models":[473],"built.":[475],"However,":[476],"since":[477],"technologies":[483],"still":[485],"development,":[487],"adequate":[488],"methods":[489,528,846,913,1023,1033,1167,1304,1361],"actively":[496],"developed":[497],"alongside":[498],"them.":[499],"Thus,":[500],"robust":[501],"benchmarks(Saelens":[502],"2019;":[505],"Aghaeepour":[506],"2016;":[509,513,709],"Weber":[510,742],"Robinson":[512,744,747],"Tran":[514],"2020)":[517],"critical":[519,1518],"adequately":[521],"evaluate":[522,1226],"both":[523],"technology":[525],"analysis.":[530,1030,1065],"issue":[533,554,563,1010,1111],"Cytometry":[535],"A":[536,552,1002,1277,1531],"features":[537,1313],"most":[541],"recent":[542],"research":[543,1442],"single":[549],"first":[553,658],"tackled":[557],"by":[558,741,900,988,1289,1334,1369,1539],"authors":[560,594,641,770,905,918,1217,1297,1375,1496],"normalization.":[568],"Van":[569],"Gassen":[570,682],"CytoNorm,":[574],"normalization":[577],"algorithm":[578,828,867],"correct":[580],"batch":[582,620,623,633],"effects":[583,634,1462],"when":[584,760,837,1184],"dealing":[585],"clinical":[587,616],"across":[589],"various":[590],"study":[591],"batches.":[592],"The":[593,769,917,1495],"propose":[595,643,825,1388],"use":[597,794,908,1553],"shared":[598],"controls":[599],"appropriate":[603],"transformations":[604,612],"each":[606],"batch,":[607],"subsequently":[609],"apply":[610,1115,1318],"remove":[622],"effects.":[624],"message":[627],"work":[630,802,1219,1368],"could":[635,991],"cell-type":[637,652],"specific,":[638],"thus":[642],"solution":[645],"based":[646,925,1000,1059,1272,1325,1418],"learns":[650],"transformations.":[654],"One":[655],"was":[665],"development":[667],"application":[669,1043,1136,1279],"range":[673],"algorithms":[676],"automated":[678,766,819,863,922],"gating":[679,767,864,923],"(Van":[681],"2015;":[685,693,721],"Finak":[686,698],"2014;":[689,713,717],"Levine":[690],"Ge":[694],"Sealfon":[696],"2012;":[697,729],"2009;":[701,705],"Lo":[702],"Chen":[706],"Shekhar":[710],"Becher":[714],"S\u00f6rensen":[718],"Zare":[722],"2010;":[725],"Linderman":[726],"Naim":[730],"2010),":[733],"were":[737,970],"evaluated":[738],"benchmarked":[740],"(Weber":[745],"issue,":[751],"Bligaard":[752],"Pedersen":[753],"Olsen":[755],"cautionary":[758],"words":[759],"blindly":[761],"trusting":[762],"output":[764],"stress":[771],"importance":[773],"multiple":[776],"get":[779],"picture":[782],"what":[784],"going":[786],"advocate":[792],"semi-supervised":[797],"approaches,":[798],"reinforcing":[799],"early":[801],"FlowCAP":[805],"challenges":[806,1015],"(Aghaeepour":[807],"2013;":[810],"Scheuermann":[811],"2013).":[814],"another":[816,1191],"article":[817,899],"population":[820],"identification,":[821],"Ji":[822],"new":[827,845,1392,1416],"interpretable":[831],"gates":[832],"fashion":[836],"label":[839,889],"available.":[842,1241],"They":[843,963,1066,1150,1242,1330,1387,1412],"develop":[844,1113],"enable":[848],"smooth":[849],"gradient-based":[850],"discrete":[853],"cost":[856],"functions,":[857],"and,":[858],"complementary":[859],"existing,":[861],"hyper-rectangular":[872],"regions":[873],"marker":[875],"space":[876],"optimized":[879],"regard":[886],"available":[891,1524],"at":[892],"level.":[895,1358],"Lucchesi":[901],"al.,":[903,1372],"discuss":[906],"computational":[910],"vaccine":[915],"studies.":[916],"classical,":[920],"existing":[921],"(FlowSOM)":[928],"tSNE":[930],"undefined":[936],"phenotypes":[937],"B":[939,951,961],"subsets":[941],"elicited":[942,987],"upon":[943],"vaccination.":[944],"Using":[945,1312],"pipeline,":[947],"identified":[949],"different":[950,1233,1278],"populations,":[953],"including":[954],"plasmablasts,":[955],"plasma":[956,968],"germinal":[959],"center":[960],"cells.":[962,1149],"find":[964],"particular":[967,1124,1551],"abundant":[972],"mice":[974],"primed":[975],"adjuvant,":[978],"highlight":[981],"intermediate":[982],"stages":[983],"differentiation":[986],"immunization":[989],"clearly":[994],"described":[995],"approaches.":[1001],"series":[1004],"articles":[1006,1108],"discusses":[1011],"image-based":[1017],"Since":[1020],"deep":[1021,1057,1116,1202,1259,1535,1548],"particularly":[1026,1036],"successful":[1027,1037],"Deep":[1031],"has":[1044],"impacted":[1046],"field.":[1049],"Su":[1050],"overview":[1055],"optical":[1063],"cover":[1067],"paradigms":[1070],"transfer":[1073],"learning,":[1074,1076,1078,1081,1549],"multimodal":[1075],"multitask":[1077],"end-to-end":[1080],"describe":[1083,1133],"segmentation,":[1088],"super-resolution":[1089],"reconstruction,":[1091,1098],"tracking,":[1093],"counting,":[1095],"cross-modal":[1096],"design":[1100],"systems.":[1106],"Several":[1107],"Mochalova":[1130],"convolutional":[1139],"neural":[1140],"networks":[1141],"(CNNs)":[1142],"quantitatively":[1144],"particle":[1146],"targeting":[1147],"conclude":[1151,1243],"CNNs":[1153],"allow":[1157,1168],"high-throughput":[1158],"object":[1159],"detection":[1160],"close-to-human":[1162],"precision.":[1163],"addition,":[1165],"objective":[1170],"analysis":[1172,1445,1507],"not":[1176,1266],"depend":[1177],"subjective":[1179],"choices":[1180],"parameter":[1182],"settings":[1183],"classical":[1186,1197,1268],"processing":[1188],"article,":[1192,1215,1295],"Lippeveld":[1193],"compare":[1196],"approaches":[1200,1204,1230,1271],"problem":[1207],"white":[1209],"blood":[1210],"classification.":[1212],"whether":[1227],"distinguish":[1232,1253],"only":[1237],"label-free":[1239,1256],"information,":[1257],"but":[1258,1569],"did":[1265],"outperform":[1267],"manually":[1274],"engineered":[1275],"Piasecka":[1290],"explore":[1298],"potential":[1300,1546],"map":[1306,1350],"activation":[1308,1339,1357],"state":[1309],"eosinophils.":[1311],"dark-field":[1315],"images":[1316,1477],"diffusion":[1319,1349],"maps":[1320],"order":[1322],"similar":[1327],"validate":[1331],"approach":[1333],"comparing":[1335],"exogenous":[1337],"eotaxin":[1341],"endogenous":[1343],"activation,":[1344],"show":[1346,1404],"correlates":[1351],"very":[1352],"measured":[1356],"Trajectory":[1359],"topic":[1365],"Verrou":[1370],"where":[1373],"signalling":[1380,1401],"dynamics":[1381],"number":[1390],"evaluation":[1393],"metrics":[1394],"quantify":[1396],"performance":[1398],"dynamics,":[1402],"trajectories":[1408],"insights.":[1411],"method":[1417],"Diffusion":[1420],"Maps":[1421],"Principal":[1423],"Curves":[1424],"showed":[1426],"great":[1427],"promise":[1428],"progression":[1432],"signal":[1434],"transduction.":[1435],"To":[1436],"support":[1437],"related":[1441],"images,":[1448],"Rose":[1449],"PySpacell,":[1453],"Python":[1455],"package":[1456],"aims":[1458],"adherent":[1464],"microscopy":[1467,1476],"images.":[1468,1572],"toolbox":[1470],"light":[1475],"situ":[1491],"transcriptomics":[1492],"metabolomics.":[1494],"took":[1497],"care":[1498],"tool":[1502],"compatible":[1503],"common":[1506],"CellProfiler,":[1511],"Fiji,":[1512],"Icy.":[1514],"Such":[1515],"efforts":[1516],"complex":[1521],"algorithmic":[1522],"audience":[1528],"researchers.":[1530],"final":[1532],"note":[1533],"provided":[1538],"Michal":[1540],"Kozubek,":[1541],"who":[1542],"reflects":[1543],"generative":[1555],"models,":[1556],"synthesis,":[1560],"goal":[1562],"render":[1567],"realistic,":[1568],"synthetic":[1570]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2020-03-13T00:00:00"}