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An Interactive Approach to Multiobjective Clustering of Gene Expression Patterns

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes
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An improved algorithm for clustering gene expression data
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Multiobjective Genetic Clustering with Ensemble Among Pareto Front Solutions: Application to MRI Brain Image Segmentation
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VAT: a tool for visual assessment of (cluster) tendency
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Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes
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Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier
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A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters
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A Cluster Separation Measure
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The Transcriptional Program in the Response of Human Fibroblasts to Serum
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Data clustering
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Incremental genetic K-means algorithm and its application in gene expression data analysis
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Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes
inferredcites →https://doi.org/10.1186/gb-2003-4-5-p3openalex-snapshot
DAVID: Database for Annotation, Visualization, and Integrated Discovery
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Analyzing microarray data using cluster analysis
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Genetic algorithms in search, optimization, and machine learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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