id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ld2ksivlgvd63cpdaez6tsr4ya	Sebastian Ohse	Blind normalization of public high-throughput databases	2019	16	.pdf	application/pdf	6587	665	53	public databases, such as related samples and features, we show that blind normalization recovery of confounding factors is formulated in the theoretical framework of compressed sensing and employs efficient optimization on manifolds. approach to the blind normalization of public high-throughput databases. Keywords Blind normalization, High-throughput data, Compressed sensing, Confounding Blind normalization of public high-throughput databases. In addition, high-throughput data based meta-analyses are best performed with high-throughput data with respect to sample information and the experimental protocol of compressed sensing it enables blind recovery of bias and subsequent normalization A database consisting of features, such as measurements of RNA, protein or metabolite and samples, such as different cell types under various stimuli, is observed. redundancies that commonly exists in high-throughput databases (see 'Blind recovery'). the case of blind recovery, high-specificity and low-sensitivity estimators can be used; as blind recovery of bias with increasing noise complexity (50×50).	./cache/work_ld2ksivlgvd63cpdaez6tsr4ya.pdf	./txt/work_ld2ksivlgvd63cpdaez6tsr4ya.txt