Comparison of three machine learning algorithms for classification of B‐cell neoplasms using clinical flow cytometry data
Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the i...
Saved in:
| Published in: | Cytometry. Part B, Clinical cytometry Vol. 106; no. 4; pp. 282 - 293 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Hoboken, USA
John Wiley & Sons, Inc
01.07.2024
Wiley Subscription Services, Inc |
| Subjects: | |
| ISSN: | 1552-4949, 1552-4957, 1552-4957 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B‐cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier. |
|---|---|
| AbstractList | Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier. Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier. |
| Author | Ng, David P. Dinalankara, Wikum Simonson, Paul D. Marchionni, Luigi |
| AuthorAffiliation | 1. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 2. Department of Pathology, University of Utah, Salt Lake City, UT |
| AuthorAffiliation_xml | – name: 2. Department of Pathology, University of Utah, Salt Lake City, UT – name: 1. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY |
| Author_xml | – sequence: 1 givenname: Wikum surname: Dinalankara fullname: Dinalankara, Wikum organization: Weill Cornell Medicine – sequence: 2 givenname: David P. orcidid: 0000-0001-8604-2073 surname: Ng fullname: Ng, David P. organization: University of Utah – sequence: 3 givenname: Luigi surname: Marchionni fullname: Marchionni, Luigi organization: Weill Cornell Medicine – sequence: 4 givenname: Paul D. surname: Simonson fullname: Simonson, Paul D. email: pds9003@med.cornell.edu organization: Weill Cornell Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38721890$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kb1uFDEUhS0URH6go0aWaCjYxfaM56dCyYokSJHShILK8niudx157MX2EK1oeASekSeJJ5NEEAnkwpbud47O8T1Ee847QOg1JUtKCPugdskvuyVjtK6foQPKOVuULa_3Ht9lu48OY7wmpOBlVb9A-0VTM9q05AD9WPlhK4OJ3mGvcdoEADxItTEOsAUZnHFrLO3aB5M2Q8TaB6ysjNFoo2Qys-7k989fCqzFDvw2TzM4xkmprHGZs1hbf4OnrAOksMO9TPIleq6ljfDq_j5CX04_Xa3OFxeXZ59XxxcLVfKmXgA0uYLmfdt2lezzqbpSQ6FZ2bRdp-umgq7XJWNStapmQDm0sqIMeAmE6uIIfZx9t2M3QK_ApSCt2AYzyLATXhrx98SZjVj774JS1lQFp9nh3b1D8N9GiEkMJk59Ze47RlEQXrQNIZRl9O0T9NqPweV-mWo4y1U4z9SbPyM9ZnlYTAbYDKjgYwyghTLp7rtzQmMFJWLavph-VHTibvtZ9P6J6MH3H3g54zfGwu6_rFh9vbo8mWW3aYDHdw |
| CitedBy_id | crossref_primary_10_1016_j_compbiomed_2025_110394 crossref_primary_10_1038_s41467_025_61279_y crossref_primary_10_1016_j_compbiomed_2025_110194 crossref_primary_10_1109_TCBBIO_2025_3562597 |
| Cites_doi | 10.1002/cyto.a.23883 10.1038/nmeth.2365 10.1109/TNNLS.2021.3084827 10.1016/j.ebiom.2018.10.042 10.1186/s13059-019-1917-7 10.1093/bioinformatics/btu677 10.1002/cyto.a.23852 10.1093/bioinformatics/bts082 10.1038/s41596-021-00550-0 10.1093/ajcp/aqaa166 10.1002/cyto.a.21007 10.1016/j.patter.2021.100351 10.1002/cyto.a.22433 10.1002/cyto.a.22837 10.1093/bioinformatics/bts300 10.1093/ajcp/aqab076 10.3390/cancers14102537 10.1016/j.hoc.2013.01.004 10.1002/cyto.a.21148 10.1093/ajcp/aqab166 10.1002/cyto.a.23904 10.1017/CBO9780511528446.003 10.1016/j.compbiomed.2013.06.004 10.1016/j.neunet.2012.09.018 10.21105/joss.00861 10.1002/cyto.a.24360 10.1093/bioinformatics/btw191 10.1002/cyto.a.24159 10.1002/cyto.a.22625 10.1002/cyto.a.20823 10.1002/cyto.a.23030 10.1109/5.58325 10.1093/bioinformatics/bty082 10.1002/cyto.a.24501 |
| ContentType | Journal Article |
| Copyright | 2024 International Clinical Cytometry Society. 2024 International Clinical Cytometry Society |
| Copyright_xml | – notice: 2024 International Clinical Cytometry Society. – notice: 2024 International Clinical Cytometry Society |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 7T5 8FD FR3 H94 P64 7X8 5PM |
| DOI | 10.1002/cyto.b.22177 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Immunology Abstracts Technology Research Database Engineering Research Database AIDS and Cancer Research Abstracts Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) AIDS and Cancer Research Abstracts Immunology Abstracts Engineering Research Database Biotechnology Research Abstracts Technology Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE MEDLINE - Academic AIDS and Cancer Research Abstracts |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Biology |
| EISSN | 1552-4957 |
| EndPage | 293 |
| ExternalDocumentID | PMC11286351 38721890 10_1002_cyto_b_22177 CYTOB22177 |
| Genre | article Journal Article Comparative Study |
| GrantInformation_xml | – fundername: National Institutes of Health funderid: U54CA273956; R01CA200859 – fundername: Department of Pathology and Laboratory Medicine at Weill Cornell Medicine, Cornell University – fundername: NCI NIH HHS grantid: U54 CA273956 – fundername: NIH HHS grantid: R01CA200859 – fundername: NIH HHS grantid: U54CA273956 – fundername: NCI NIH HHS grantid: R01 CA200859 |
| GroupedDBID | -~X .GA .Y3 05W 0R~ 10A 1L6 1OC 24P 31~ 33P 3WU 4.4 4ZD 50Y 51W 51X 52N 52O 52P 52S 52T 52W 52X 53G 5GY 5VS 702 7PT 8-1 8-4 8-5 8UM 930 A03 AAEVG AAHQN AAMNL AANLZ AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ACAHQ ACCZN ACFBH ACGFO ACGFS ACIWK ACPOU ACPRK ACSCC ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADZMN AEIGN AEIMD AEUQT AEUYR AFBPY AFFNX AFFPM AFGKR AFPWT AFRAH AFWVQ AFZJQ AHBTC AIAGR AITYG AIURR ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ATUGU BAWUL BFHJK BRXPI BY8 CS3 D-F DCZOG DIK DR2 DRFUL DRSTM E3Z EBD EBS EJD EMOBN F00 F01 F04 F5P G-S GNP GODZA HBH HF~ HGLYW HHY HHZ IX1 KQQ LATKE LAW LEEKS LH4 LOXES LP6 LP7 LUTES LYRES MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM O9- OIG OK1 P2P P2W P4D QB0 QRW RNS ROL RWI RX1 SUPJJ SV3 UB1 V2E W99 WIH WIN WJL WQJ WRC XG1 XV2 ZZTAW AAYXX AGHNM AGYGG CITATION O8X CGR CUY CVF ECM EIF NPM 7QO 7T5 8FD FR3 H94 P64 7X8 5PM |
| ID | FETCH-LOGICAL-c4587-ee8949f5d99b6adada6b4fe3f2489bbf786ebdf422ac9c72e15e9a612e54e01f3 |
| IEDL.DBID | WIN |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001219616100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1552-4949 1552-4957 |
| IngestDate | Tue Nov 04 02:04:48 EST 2025 Thu Oct 02 10:51:26 EDT 2025 Fri Jul 25 12:09:06 EDT 2025 Thu Jul 03 03:55:15 EDT 2025 Sat Nov 29 03:26:14 EST 2025 Tue Nov 18 21:39:54 EST 2025 Wed Jan 22 17:17:31 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | B‐cell neoplasms deep learning flow cytometry machine learning |
| Language | English |
| License | 2024 International Clinical Cytometry Society. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4587-ee8949f5d99b6adada6b4fe3f2489bbf786ebdf422ac9c72e15e9a612e54e01f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-8604-2073 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/11286351 |
| PMID | 38721890 |
| PQID | 3085294955 |
| PQPubID | 2030116 |
| PageCount | 12 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_11286351 proquest_miscellaneous_3053980012 proquest_journals_3085294955 pubmed_primary_38721890 crossref_citationtrail_10_1002_cyto_b_22177 crossref_primary_10_1002_cyto_b_22177 wiley_primary_10_1002_cyto_b_22177_CYTOB22177 |
| PublicationCentury | 2000 |
| PublicationDate | July 2024 |
| PublicationDateYYYYMMDD | 2024-07-01 |
| PublicationDate_xml | – month: 07 year: 2024 text: July 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken, USA |
| PublicationPlace_xml | – name: Hoboken, USA – name: United States – name: Hoboken |
| PublicationTitle | Cytometry. Part B, Clinical cytometry |
| PublicationTitleAlternate | Cytometry B Clin Cytom |
| PublicationYear | 2024 |
| Publisher | John Wiley & Sons, Inc Wiley Subscription Services, Inc |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc |
| References | 2012; 81 1990; 78 2019; 95 2021; 2 2013; 27 2013; 43 2015; 31 2016; 32 2011; 79 2011; 12 2014; 85 1995; 1 2022; 157 97 2022; 101 2010; 77A 2021; 16 2013; 37 2018; 3 2021; 99 2020; 97 2019; 20 2013; 10 2021; 156 2015; 87 2022; 14 2017 2016 2012; 28 2021; 155 2022; 33 2018; 34 2018; 37 2016; 89 1988 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 Abadi M. (e_1_2_8_2_1) 2016 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 Ho T. K. (e_1_2_8_14_1) 1995 Lundberg S.M. (e_1_2_8_20_1) 2017 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 e_1_2_8_33_1 Pedregosa F. (e_1_2_8_28_1) 2011; 12 e_1_2_8_30_1 |
| References_xml | – volume: 34 start-page: 2245 year: 2018 end-page: 2253 article-title: flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry publication-title: Bioinformatics – volume: 78 start-page: 1464 year: 1990 end-page: 1480 article-title: The self‐organizing map publication-title: Proc. IEEE – volume: 89 start-page: 1084 year: 2016 end-page: 1096 article-title: Comparison of clustering methods for high‐dimensional single‐cell flow and mass cytometry data publication-title: Cytometry. Part A – start-page: 265 year: 2016 end-page: 283 – volume: 10 start-page: 228 year: 2013 end-page: 238 article-title: Critical assessment of automated flow cytometry data analysis techniques publication-title: Nature Methods – volume: 14 year: 2022 article-title: Artificial intelligence enhances diagnostic flow cytometry workflow in the detection of minimal residual disease of chronic lymphocytic leukemia publication-title: Cancers – volume: 37 start-page: 52 year: 2013 end-page: 65 article-title: Essentials of the self‐organizing map. Neural Netw publication-title: Twenty‐fifth Anniversay Commemorative Issue – volume: 33 start-page: 6999 year: 2022 end-page: 7019 article-title: A survey of convolutional neural networks: Analysis, applications, and prospects publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 31 year: 1988 end-page: 40 – volume: 28 start-page: 1009 year: 2012 end-page: 1016 article-title: Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T‐cell flow cytometry assays publication-title: Bioinformatics – volume: 3 start-page: 861 year: 2018 article-title: UMAP: Uniform manifold approximation and projection publication-title: J. Open Source Softw. – volume: 16 start-page: 3775 year: 2021 end-page: 3801 article-title: Analyzing high‐dimensional cytometry data using FlowSOM publication-title: Nat. Protoc. – start-page: 4768 year: 2017 end-page: 4777 article-title: A unified approach to interpreting model predictions – volume: 97 start-page: 107 year: 2020 end-page: 112 article-title: Improving the rigor and reproducibility of flow cytometry‐based clinical research and trials through automated data analysis publication-title: Cytometry. Part A: the Journal of the International Society for Analytical Cytology – volume: 81 start-page: 25 year: 2012 end-page: 34 article-title: Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data publication-title: Cytometry. Part A: the Journal of the International Society for Analytical Cytology – volume: 28 start-page: 2052 year: 2012 end-page: 2058 article-title: flowPeaks: a fast unsupervised clustering for flow cytometry data via K‐means and density peak finding publication-title: Bioinforma. Oxf. Engl. – volume: 20 start-page: 297 year: 2019 article-title: A comparison framework and guideline of clustering methods for mass cytometry data publication-title: Genome Biology – volume: 27 start-page: 251 year: 2013 end-page: 265 article-title: MBL versus CLL: how important is the distinction? publication-title: Hematol. Oncol. Clin. North Am. – volume: 156 start-page: 1092 year: 2021 end-page: 1102 article-title: De Novo identification and visualization of important cell populations for classic Hodgkin lymphoma using flow cytometry and machine learning publication-title: Am. J. Clin. Pathol. – volume: 79 start-page: 6 year: 2011 end-page: 13 article-title: Rapid cell population identification in flow cytometry data publication-title: Cytom Part J. Int. Soc. Anal. Cytol. – volume: 87 start-page: 636 year: 2015 end-page: 645 article-title: FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data publication-title: Cytometry, Part A – volume: 97 publication-title: Cytometry A – volume: 85 start-page: 277 year: 2014 end-page: 286 article-title: High‐throughput flow cytometry data normalization for clinical trials publication-title: Cytometry. Part A – volume: 157 start-page: 443 year: 2022 end-page: 450 article-title: Potential for process improvement of clinical flow cytometry by incorporating real‐time automated screening of data to expedite addition of antibody panels publication-title: American Journal of Clinical Pathology – volume: 89 start-page: 461 year: 2016 end-page: 471 article-title: flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data publication-title: Cytometry. Part A – volume: 97 start-page: 268 year: 2020 end-page: 278 article-title: CytoNorm: A normalization algorithm for cytometry data publication-title: Cytometry. Part A – volume: 32 start-page: 2473 year: 2016 end-page: 2480 article-title: flowAI: Automatic and interactive anomaly discerning tools for flow cytometry data publication-title: Bioinformatics – volume: 95 start-page: 966 year: 2019 end-page: 975 article-title: Automated flow cytometric MRD assessment in childhood acute B‐ lymphoblastic leukemia using supervised machine learning publication-title: Cytometry. Part A: the Journal of the International Society for Analytical Cytology – volume: 31 start-page: 606 year: 2015 end-page: 607 article-title: flowDensity: Reproducing manual gating of flow cytometry data by automated density‐based cell population identification publication-title: Bioinformatics – volume: 43 start-page: 1192 year: 2013 end-page: 1195 article-title: Discrimination of malignant neutrophils of chronic myelogenous leukemia from normal neutrophils by support vector machine publication-title: Computers in Biology and Medicine – volume: 101 start-page: 325 year: 2022 end-page: 338 article-title: PeacoQC: Peak‐based selection of high quality cytometry data publication-title: Cytometry. Part A – volume: 77A start-page: 121 year: 2010 end-page: 131 article-title: Per‐channel basis normalization methods for flow cytometry data publication-title: Cytometry. Part A – volume: 12 start-page: 2825 year: 2011 end-page: 2830 article-title: Scikit‐learn: Machine learning in python publication-title: J. Mach. Learn. Res. – volume: 1 start-page: 278 year: 1995 end-page: 282 – volume: 99 start-page: 814 year: 2021 end-page: 824 article-title: Computational flow cytometry as a diagnostic tool in suspected‐myelodysplastic syndromes publication-title: Cytometry. Part A – volume: 37 start-page: 91 year: 2018 end-page: 100 article-title: Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome publication-title: eBioMedicine – volume: 155 start-page: 597 year: 2021 end-page: 605 article-title: Augmented human intelligence and automated diagnosis in flow cytometry for hematologic malignancies publication-title: Am. J. Clin. Pathol. – volume: 2 year: 2021 article-title: Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms publication-title: Patterns – ident: e_1_2_8_6_1 doi: 10.1002/cyto.a.23883 – ident: e_1_2_8_4_1 doi: 10.1038/nmeth.2365 – ident: e_1_2_8_18_1 doi: 10.1109/TNNLS.2021.3084827 – ident: e_1_2_8_15_1 doi: 10.1016/j.ebiom.2018.10.042 – ident: e_1_2_8_19_1 doi: 10.1186/s13059-019-1917-7 – ident: e_1_2_8_22_1 doi: 10.1093/bioinformatics/btu677 – ident: e_1_2_8_30_1 doi: 10.1002/cyto.a.23852 – ident: e_1_2_8_3_1 doi: 10.1093/bioinformatics/bts082 – ident: e_1_2_8_29_1 doi: 10.1038/s41596-021-00550-0 – volume: 12 start-page: 2825 year: 2011 ident: e_1_2_8_28_1 article-title: Scikit‐learn: Machine learning in python publication-title: J. Mach. Learn. Res. – ident: e_1_2_8_26_1 doi: 10.1093/ajcp/aqaa166 – ident: e_1_2_8_5_1 doi: 10.1002/cyto.a.21007 – ident: e_1_2_8_23_1 doi: 10.1016/j.patter.2021.100351 – ident: e_1_2_8_9_1 doi: 10.1002/cyto.a.22433 – ident: e_1_2_8_11_1 doi: 10.1002/cyto.a.22837 – ident: e_1_2_8_12_1 doi: 10.1093/bioinformatics/bts300 – ident: e_1_2_8_35_1 doi: 10.1093/ajcp/aqab076 – ident: e_1_2_8_31_1 doi: 10.3390/cancers14102537 – ident: e_1_2_8_32_1 doi: 10.1016/j.hoc.2013.01.004 – ident: e_1_2_8_10_1 doi: 10.1002/cyto.a.21148 – ident: e_1_2_8_34_1 doi: 10.1093/ajcp/aqab166 – ident: e_1_2_8_36_1 doi: 10.1002/cyto.a.23904 – ident: e_1_2_8_33_1 doi: 10.1017/CBO9780511528446.003 – ident: e_1_2_8_27_1 doi: 10.1016/j.compbiomed.2013.06.004 – ident: e_1_2_8_17_1 doi: 10.1016/j.neunet.2012.09.018 – ident: e_1_2_8_24_1 doi: 10.21105/joss.00861 – start-page: 278 volume-title: Proceedings of 3rd International Conference on Document Analysis and Recognition year: 1995 ident: e_1_2_8_14_1 – ident: e_1_2_8_7_1 doi: 10.1002/cyto.a.24360 – start-page: 4768 volume-title: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 year: 2017 ident: e_1_2_8_20_1 – ident: e_1_2_8_25_1 doi: 10.1093/bioinformatics/btw191 – ident: e_1_2_8_39_1 doi: 10.1002/cyto.a.24159 – ident: e_1_2_8_37_1 doi: 10.1002/cyto.a.22625 – ident: e_1_2_8_13_1 doi: 10.1002/cyto.a.20823 – ident: e_1_2_8_38_1 doi: 10.1002/cyto.a.23030 – ident: e_1_2_8_16_1 doi: 10.1109/5.58325 – ident: e_1_2_8_21_1 doi: 10.1093/bioinformatics/bty082 – start-page: 265 volume-title: TensorFlow: a system for large‐scale machine learning, in: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI’16 year: 2016 ident: e_1_2_8_2_1 – ident: e_1_2_8_8_1 doi: 10.1002/cyto.a.24501 |
| SSID | ssj0035467 |
| Score | 2.3763487 |
| Snippet | Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and... |
| SourceID | pubmedcentral proquest pubmed crossref wiley |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 282 |
| SubjectTerms | Accuracy Algorithms Artificial intelligence B-Lymphocytes - classification B-Lymphocytes - immunology B-Lymphocytes - pathology B‐cell neoplasms Classification deep learning Flow cytometry Flow Cytometry - methods Humans Immunophenotyping - methods Learning algorithms Lymphoma, B-Cell - classification Lymphoma, B-Cell - diagnosis Lymphoma, B-Cell - pathology Machine Learning Neoplasms |
| Title | Comparison of three machine learning algorithms for classification of B‐cell neoplasms using clinical flow cytometry data |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcyto.b.22177 https://www.ncbi.nlm.nih.gov/pubmed/38721890 https://www.proquest.com/docview/3085294955 https://www.proquest.com/docview/3053980012 https://pubmed.ncbi.nlm.nih.gov/PMC11286351 |
| Volume | 106 |
| WOSCitedRecordID | wos001219616100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1552-4957 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035467 issn: 1552-4949 databaseCode: WIN dateStart: 20030101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1552-4957 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035467 issn: 1552-4949 databaseCode: DRFUL dateStart: 20030101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bb9MwFD6CcREvXMZlgVEZCZ5QtiaxE_uRFSqQRkFog_IU2a7dVWqTqclAFS_8BH4jv4RjJw2rJpAQihRF8rFiH_vYn-3j7wA8zaSVMksnoWYJwwVKFIVKqhRf0vZTkcrUk-l8PMxGIz4ei_fthpu7C9PwQ3Qbbs4y_HjtDFyqav83aahe1eWe2osRU7vL5BH1dvnpzWg9ECeM-gCyjmQsdCQsrd87Zt8_n3lzRroAMy96S55HsX4aGt763wrchpstACUvmh5zBy6ZYhuuNSEpV9tw_W172H4Xvg26GIWktKTGVjdk4Z0vDWmjTUyJnE_L5aw-WVQE8S_RDo079yPf4i7fwc_vP9zxACmcs7qsUNA520_J-lImsfPyK3HlXJh6uSLOafUeHA9fHQ1eh22shlBThuOUMRw1bNlECJXKCT6potYkNqZcKGUznho1sTSOpRY6i03EjJAIrwyjph_Z5D5sFWVhdoCkNJOIU5TWOqM6iUUmEFNyKxjVzPZVAM_X7ZXrlsjcxdOY5w0Fc5y7Eucq95oN4FknfdoQePxBbnfd9HlrxlWeICCNsV6MBfCkS0YDdGqTqLYzJ8MSwR1sDOBB01O6HyUcF9hc9APgG32oE3Dk3pspxezEk3wjDuYIBqMAQt-J_lr4fPD56N2B_3z4j_KP4EaMOK3xQN6FrXp5Zh7DVf2lnlXLHlzOxrwHV15-GB4f9rx5_QJwKiyT |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bb9MwFD6CjtsLl3ELDDASPKFsTWIn9iMrVEN0BaEOjafIdu2uUpugNgNVvPAT-I38Eo6dNKyaQEIoUhTJx45vx_5sH38H4FkmrZRZOg41SxguUKIoVFKl-JK2m4pUpp5M5-MgGw758bF43_g5dXdhan6IdsPNaYYfr52Cuw3pvd-soXpVlbtqN0ZQnV2ELYqpsg5svfrQPxqsB-OEUe9E1hGNhY6IpbF9xxT2zsbfnJXOQc3zFpNnkayfivo3_rsQN-F6g0LJy7rb3IILptiGy7VfytU2XDlsTtxvw7de66iQlJZU2PSGzL0FpiGNy4kJkbNJuZhWJ_MlQRBMtIPkzgbJN7uLt__z-w93RkAKZ7EulyjoLO4nZH0zk9hZ-ZW4fM5NtVgRZ7l6B476r0e9g7Bx2BBqynCwMoZjFVs2FkKlcoxPqqg1iY0pF0rZjKdGjS2NY6mFzmITMSMkYizDqOlGNrkLnaIszH0gKc0kghWltc6oTmKRCQSW3ApGNbNdFcCLdYPlumEzd041ZnnNwxznLse5yn3NBvC8lf5cs3j8QW5n3fZ5o8vLPEFUGmO5GAvgaRuMWuiqTWK1nToZlgjusGMA9-qu0v4o4bjK5qIbAN_oRK2AY_jeDCmmJ57pG8EwR0QYBRD6XvTXzOe9T6N3-_7zwT_KP4GrB6PDQT54M3z7EK7FCNxqk-Qd6FSLU_MILukv1XS5eNzo1y8_sy9X |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dbtMwFD6CDabd8DP-AgOMBFcoW5vYiX3JOioQpUxoQ9tVZDt2V6lNpjYbqrjhEXhGnoRjJw2rJpAQihRF8nFiH_vYn-Pj7wC8TKWVMk3yULOY4QKl2w2VVAnepO0kIpGJJ9P5MkiHQ358LA6aOKfuLEzND9H-cHOW4cdrZ-DmLLe7v1lD9aIqd9ROhKA6vQ7rlIkELXN9_3P_aLAcjGNGfRBZRzQWOiKWxvcd37B7Of_qrHQFal71mLyMZP1U1L_935W4A7caFEre1N3mLlwzxRbcrONSLrZg42Oz434PvvXaQIWktKTCpjdk6j0wDWlCToyInIzK2bg6nc4JgmCiHSR3Pki-2V2-vZ_ff7g9AlI4j3U5R0HncT8iy5OZxE7Kr8SVc2qq2YI4z9X7cNR_e9h7FzYBG0JNGQ5WxnBUsWW5ECqROV6JotbENqJcKGVTnhiVWxpFUgudRqbLjJCIsQyjptO18QNYK8rCPAKS0FQiWFFa65TqOBKpQGDJrWBUM9tRAbxeNlimGzZzF1RjktU8zFHmSpypzGs2gFet9FnN4vEHue1l22eNLc-zGFFphPViLIAXbTJaoVObRLWdOxkWC-6wYwAP667SfijmuMrmohMAX-lErYBj-F5NKcannukbwTBHRNgNIPS96K-Fz3onh5_2_OPjf5R_DhsH-_1s8H744QlsRojbao_kbVirZufmKdzQF9V4PnvWmNcvib0u0g |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparison+of+three+machine+learning+algorithms+for+classification+of+B%E2%80%90cell+neoplasms+using+clinical+flow+cytometry+data&rft.jtitle=Cytometry.+Part+B%2C+Clinical+cytometry&rft.au=Dinalankara%2C+Wikum&rft.au=Ng%2C+David+P&rft.au=Marchionni%2C+Luigi&rft.au=Simonson%2C+Paul+D&rft.date=2024-07-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=1552-4949&rft.eissn=1552-4957&rft.volume=106&rft.issue=4&rft.spage=282&rft.epage=293&rft_id=info:doi/10.1002%2Fcyto.b.22177&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1552-4949&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1552-4949&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1552-4949&client=summon |