Assessment and Improvement of Statistical Tools for Comparative Proteomics Analysis of Sparse Data Sets with Few Experimental Replicates

2013 ◽  
Vol 12 (9) ◽  
pp. 3874-3883 ◽  
Author(s):  
Veit Schwämmle ◽  
Ileana Rodríguez León ◽  
Ole Nørregaard Jensen
Keyword(s):  
Sparse Data ◽  
Comparative Proteomics ◽  
Data Sets ◽  
Statistical Tools ◽  
Proteomics Analysis ◽  
Sparse Data Sets

Inversion for source parameters from sparse data sets: Test of the method and application to the 1951 (M=6.9) Kona, Hawaii, earthquake

1994 ◽  
Vol 99 (B10) ◽  
pp. 19661-19678 ◽  
Author(s):  
Martin Beisser ◽  
Dominique Gillard ◽  
Max Wyss
Keyword(s):  
Source Parameters ◽  
Sparse Data ◽  
Data Sets ◽  
Sparse Data Sets

scholarly journals Evaluating parameters for ligand-based modeling with random forest on sparse data sets

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander Kensert ◽  
Jonathan Alvarsson ◽  
Ulf Norinder ◽  
Ola Spjuth
Keyword(s):  
Random Forest ◽  
Sparse Data ◽  
Data Sets ◽  
Sparse Data Sets

The Gamma Matrix to Summarize Dense and Sparse Data Sets for Big Data Analytics

2016 ◽  
Vol 28 (7) ◽  
pp. 1905-1918 ◽  
Author(s):  
Carlos Ordonez ◽  
Yiqun Zhang ◽  
Wellington Cabrera
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Sparse Data ◽  
Data Sets ◽  
Sparse Data Sets

An Advanced Group Contribution Method for High-Dimensional, Sparse Data Sets

2011 ◽  
Vol 31 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Chang Jun Lee ◽  
Jong Min Lee
Keyword(s):  
Sparse Data ◽  
Group Contribution ◽  
High Dimensional ◽  
Data Sets ◽  
Group Contribution Method ◽  
Sparse Data Sets

An improved locally linear embedding for sparse data sets

2010 ◽  
Author(s):  
Ying Wen ◽  
Zhenyu Zhou ◽  
Xunheng Wang ◽  
Yudong Zhang ◽  
Renhua Wu
Keyword(s):  
Sparse Data ◽  
Locally Linear Embedding ◽  
Data Sets ◽  
Linear Embedding ◽  
Sparse Data Sets ◽  
Locally Linear
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