Analyzing Large Data Sets to Find Deviation PatternsOperations, such as data processing operations, can be improved by applying clustering and statistical techniques to observed behaviors in the data processing operations.Sengupta, ArijitStronger, Brad A....
ANALYZING LARGE DATA SETS USING A COMPUTER SYSTEMA method and/or system for making determinations regarding samples from biologic sources. A computer implemented method and/or system can be used to automate parts of the analysis.Boris Fain
7.B Analyzing Large Data Set 1. Heatmap: From HHMI : A –free 26 slide tutorial on how to analyze DNA microarray data http://www.hhmi.org/biointeractive/howanalyze-dna-microarray-data 2. Statistical tests: T-tests can be used to test if two sets of data are significantly different from...
New methods for parsimony analysis of large data sets are presented. The new methods are sectorial searches, tree-drifting, and tree-fusing. For Chase et al. 's 500-taxon data set these methods (on a 266-MHz Pentium II) find a shortest tree in less than 10 min (i.e., over 15,000...
Clark NR, Ma'ayan A: Introduction to statistical methods for analyzing large data sets: gene-set enrichment analysis. Science signaling 2011, 4(190):tr4.Clark NR, Ma'ayan A: Introduction to statistical methods for analyzing large data sets: gene-set enrichment analysis. Sci...
Low-rank matrix factorization and co-clustering algorithms for analyzing large data sets 来自 Semantic Scholar 喜欢 0 阅读量: 28 作者:A Donavall,M Rege,X Liu,K Jafari-Khouzani 摘要: With the ever increasing data, there is a greater need for analyzing and extracting useful and meaningful ...
Sets, Bags, and Rock and Roll: Analyzing Large Data Sets of Network Data. As network traffic increases, the problems associated with monitoring and analyzing the traffic on high speed networks become increasingly difficult. In this paper, we introduce a new conceptual framework based on sets of ...
Across the data sets, the multiple iHMMs are learned jointly in a MTL setting, employing a nested Dirichlet process (nDP). The nDP-iHMM MTL method allows simultaneous task-level and data-level clustering, with which the individual iHMMs are enhanced and the between-task similarities are ...
It often applied to the analysis of large data sets in analytical chemistry [13], medical statistics [14], pharmaceutical engineering [15, 16]. VIP analysis method used in the analysis of clinical risk factors, which helps to understand the influence of risk factors on the severity of AEs. ...
the data. The application allows users to inspect the gene expression patterns of subpopulations through annotated gene sets and pathways, including Gene Ontology (GO) categories. Users may also highlight certain clusters and perform differential expression from their browsers via the frontend application...