Machine learning for flow cytometry data analysis
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Summary
This thesis presents novel algorithms for fitting multivariate Gaussian mixture models to data that is truncated, censored, or truncated and censored and proposes a transfer learning technique combined with the low-density separation principle.
- Type
- dissertation
- Published
- 2011-01-01
- Cited by
- 2
- References
- 77
- OpenAlex
- https://openalex.org/W38918204
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59626473
Keywords
Computer science, Cluster analysis, Artificial intelligence, Process (computing), Data mining
References
- Generation of flow cytometry data files with a potentially infinite number of dimensions
- Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation
- Boosting Expert Ensembles for Rapid Concept Recall
- The Moment Generating Function of the Truncated Multi‐Normal Distribution
- Statistical Matching: "A Frequentist Theory, Practical Applications, And Alternative Bayesian Approaches"
- Multivariate analysis by data depth: descriptive statistics, graphics and inference, (with discussion and a rejoinder by Liu and Singh)
- Practical Flow Cytometry
- Statistical Comparisons of Classifiers over Multiple Data Sets
- Seventeen-colour flow cytometry: unravelling the immune system
- Incidence of phenotypic aberrations in a series of 467 patients with B chronic lymphoproliferative disorders: basis for the design of specific four-color stainings to be used for minimal residual disease investigation
- Nested Support Vector Machines
- Comparison of Methods for the Computation of Multivariate t Probabilities
- Numerical computation of multivariate t-probabilities with application to power calculation of multiple contrasts
- Optimizing transformations for automated, high throughput analysis of flow cytometry data
- Bayesian clustering of flow cytometry data for the diagnosis of B-Chronic Lymphocytic Leukemia
- Parameterization and Bayesian Modeling
- Computation of the bivariate normal integral
- Automated gating of flow cytometry data via robust model‐based clustering
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Statistical Mixture Modeling for Cell Subtype Identification in Flow Cytometry
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