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The WGCNA method partitions genes expressed in Caco-2 cell lines into groups enriched for topological overlap based on their expression profiles.
By use of phased single-nucleotide polymorphism data, our method partitions a chromosome into a series of adjacent, nonoverlapping blocks.
The method partitions the input data into small chunks, which are then rearranged into groups so different groups of chunks can be processed in parallel.
A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition.
We also present our method partitioning universe to many small regions.
The method partitions the network nodes so that it maximizes the network's modularity.
The method partitions the network nodes in a way that maximizes the network's modularity.
The proposed method partitions first image roughly into particle region and background region using Otsu's thresholding.
This method partitions the entire dataset into K subsets or folds Fi, i = 1,2.
This method partitions branches of a specified tree into groups according to dN/dS.
Our method partitions the samples of majority class into N nonoverlapped groups, with each group joined by minority samples.
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