id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
10_1101-2021_01_08_425967	Camacho-Hernández, Diego A.	Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data	2021	9	.pdf	application/pdf	4220	355	56	Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data noise in clustered omics and systems biology data noise is embedded in the resulting clustered vector. clustering algorithm orders the data, with several measures regarding external and internal classification yielded in clustering analysis of the data. Such partition vector is colored according to the classification that each item is associated cluster, this noise may be due to the intrinsic metric or marker used to order the data set. to a vector of numeric labels, in which a number represents a classification, to be able to calculate SC. Effect of the length and number of partitions of the vector in the Z-score distributions. statistical significance of the PQA score because of the less the number of items in the VP, the greater Finally, to assess the PQA methodology using systems biology data we clustered 210 networks 	./cache/10_1101-2021_01_08_425967.pdf	./txt/10_1101-2021_01_08_425967.txt