title: Clinical Outcome Prediction Using Single-Cell Data.
abstract: Single-cell technologies like flow cytometry (FCM) provide valuable biological data for knowledge discovery in complex cellular systems like tissues and organs. FCM data contains multi-dimensional information about the cellular heterogeneity of intricate cellular systems. It is possible to correlate single-cell markers with phenotypic properties of those systems. Cell population identification and clinical outcome prediction from single-cell measurements are challenging problems in the field of single cell analysis. In this paper, we propose a hybrid learning approach to predict clinical outcome using samples' single-cell FCM data. The proposed method is efficient in both i) identification of cellular clusters in each sample's FCM data and ii) predict clinical outcome (healthy versus unhealthy) for each subject. Our method is robust and the experimental results indicate promising performance.
authors.forename: Maziyar Baran
authors.initials: MB
authors.lastname: Pouyan
authors.forename: Vasu
authors.initials: V
authors.lastname: Jindal
authors.forename: Mehrdad
authors.initials: M
authors.lastname: Nourani
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D002478:Cells, Cultured / Q000378:metabolism* / Q000473:pathology*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D046888:Tissue Array Analysis / Q000379:methods*
chemical_list: D015415:Biomarkers