In this work we apply two clustering algorithms, K-means and Expectation Maximization to particular a problem and we compare the groupings obtained on the basis ...
Clustering is widely used in the analysis of microarray data to group genes of interest targeted from microarray experiments on the basis of similarity of ...
In this work we apply two clustering algorithms, K-means and expectation maximization to particular a problem and we compare the groupings obtained on the basis ...
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Classification of Gene Expression Profiles: Comparison of K-means and. Expectation Maximization Algorithms. Cristina Rubio-Escudero. Dpto. Lenguajes y Sistemas ...
Classification of Gene Expression Profiles: Comparison of K-means and Expectation Maximization Algorithms. Creators: Rubio Escudero, Cristina · Martínez Álvarez ...
The advent of DNA microarray technology has enabled biologists to monitor the expression levels (MRNA) of thousands of genes simultaneously.
Classification of Gene Expression Profiles: Comparison of K-means and Expectation Maximization Algorithms. Autor/es, Rubio Escudero, Cristina · Martínez Álvarez ...
In microarray research, finding groups of genes exhibiting similar expressions, clustering and biclustering techniques are more commonly used in gene expression ...
Clustering performance comparison using K-means and expectation ...
pmc.ncbi.nlm.nih.gov › PMC4433949
Both of the clustering methods tested showed better accuracy than that achieved solely by classifying the experimental data with the logistic algorithm WEKA.
Missing: Expression Profiles:
Classification of Gene Expression Profiles: Comparison of K-means and Expectation Maximization Algorithms. Rubio-Escudero C., Martínez-Álvarez F., Romero ...