Research map: Automated B ‐cell and plasma cell identification using unsupervised clustering by FlowSOM and an excel‐based classification model

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  1. High-performance medicine: the convergence of human and artificial intelligence · Eric J. Topol · 2018 · 10180 citations · Cited by this paper
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  21. Multiparametric flow cytometry profiling of neoplastic plasma cells in multiple myeloma · Hans Erik Johnsen · 2010 · 35 citations · Cited by this paper
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  24. Evaluation of CD229 as a new alternative plasma cell gating marker in the flow cytometric immunophenotyping of monoclonal gammopathies · Prashant Ramesh Tembhare · 2018 · 21 citations · Cited by this paper
  25. Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence‐assisted workflow · Alexandre Bazinet · 2023 · 19 citations · Cited by this paper
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  27. Comparison of three machine learning algorithms for classification of B‐cell neoplasms using clinical flow cytometry data · Wikum Dinalankara · 2024 · 12 citations · Cited by this paper
  28. Artificial intelligence accelerates the interpretation of measurable residual B lymphoblastic leukemia by flow cytometry · Jansen N. Seheult · 2025 · 11 citations · Cited by this paper
  29. GateNet: A novel neural network architecture for automated flow cytometry gating · L. Fisch · 2024 · 8 citations · Cited by this paper
  30. From gating to computational flow cytometry: Exploiting artificial intelligence for MRD diagnostics · Giovanni Riva · 2023 · 4 citations · Cited by this paper
  31. SLAM Family Member “CD229”: A Novel Gating Marker for Plasma Cells in Flow Cytometric Immunophenotyping (FCI) of Multiple Myeloma (MM) · Sitaram Ghogale · 2017 · 3 citations · Cited by this paper
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  35. Smartcytoflow: A Machine Learning Decision Support System for Flow Cytometry Analysis in Multiple Myeloma Diagnosis and Monitoring · Carlos Pérez Míguez · 2024 · 1 citation · Cited by this paper
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