id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
10_1101-2021_02_11_430695	Gordon-Rodriguez, Elliott	Learning Sparse Log-Ratios for High-Throughput Sequencing Data	2021	12	.pdf	application/pdf	7973	817	60	Log-ratios are an important class of features for analyzing high-throughput sequencing (HTS) metagenomic data for HTS data, and more generally, high-dimensional CoDa. Unlike existing methods, CoDaCoRe is simultaneously scalable, interpretable, sparse, and accurate. unlabelled datasets, {xi}ni=1, as a method for identiLearning Sparse Log-Ratios for High-Throughput Sequencing Data CoDaCoRe variable selection for the first (most explanatory) log-ratio on the Crohn disease data (Rivera-Pinto et al., 2018). more generally, in the field of CoDa. Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data	./cache/10_1101-2021_02_11_430695.pdf	./txt/10_1101-2021_02_11_430695.txt