gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data

No abstract is available for this paper.

• Publication year

  2015

• Venue

  Cytometry Part A

• Publication date

  2015-07-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1002/cyto.a.22622PMID 25572884
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Critical assessment of automated flow cytometry data analysis techniques

  2013cited by this paper
• Translational Applications of Flow Cytometry in Clinical Practice

  2012cited by this paper
• Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data

  2011cited by this paper
• Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

  2010cited by this paper
• Optimizing transformations for automated, high throughput analysis of flow cytometry data

  2010cited by this paper
• Virtual screening of bioassay data

  2009cited by this paper
• Application of 3D Zernike descriptors to shape-based ligand similarity searching

  2009cited by this paper
• The WEKA data mining software: an update

  2009cited by this paper
• Automated high-dimensional flow cytometric data analysis

  2009cited by this paper
• Flow cytometry histograms: Transformations, resolution, and display

  2008cited by this paper
• Mixture modeling approach to flow cytometry data

  2008cited by this paper
• High-content single-cell drug screening with phosphospecific flow cytometry.

  2008cited by this paper
• A Review on

  2008cited by this paper
• What is the expectation maximization algorithm?

  2008cited by this paper
• Automated gating of flow cytometry data via robust model‐based clustering

  2008cited by this paper
• The Expectation-Maximization Algorithm

  2007cited by this paper
• A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data

  2006cited by this paper
• Cytomics as a new potential for drug discovery.

  2006cited by this paper
• Interpreting flow cytometry data: a guide for the perplexed

  2006cited by this paper
• Hyperlog—A flexible log‐like transform for negative, zero, and positive valued data

  2005cited by this paper
• Flow cytometry for high-throughput, high-content screening.

  2004cited by this paper
• Flow cytometry in clinical cancer research.

  1983cited by this paper
• Clinical application of flow cytometry: a review.

  1981cited by this paper

• Using Neural Networks to Identify Features Associated with HIV Nef Protein and Cancer

  2020cites this paper
• Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

  2019cites this paper
• Identifying differential cell populations in flow cytometry data accounting for marker frequency

  2019cites this paper
• Automated identification of maximal differential cell populations in flow cytometry data

  2019cites this paper
• Pathway Analysis of Marker Genes for Leukemia Cancer using Enhanced Genetic Algorithm-Neural Network (enGANN)

  2018cites this paper
• Identifying Cell Populations in Flow Cytometry Data Using Phenotypic Signatures

  2017cites this paper
• Guidelines for the use of flow cytometry and cell sorting in immunological studies.

  2017cites this paper
• Feature-based Comparison of Flow Cytometry Data

  2017cites this paper
• Automated analysis of flow cytometry data comes of age

  2016cites this paper
• A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

  2016cites this paper
• FloReMi: Flow density survival regression using minimal feature redundancy

  2016cites this paper
• Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.

  2015cites this paper
• Immune Cell Phenotyping Using Flow Cytometry

  2005cites this paper