A novel graphical representation of proteins and its application

2012 ◽  
Vol 391 (1-2) ◽  
pp. 93-99 ◽  
Author(s):  
Ping-an He ◽  
Jinzhou Wei ◽  
Yuhua Yao ◽  
Zhixin Tie
Keyword(s):  
Graphical Representation ◽  
Graphical Representation Of Proteins

Graphical Representation of Proteins†

2011 ◽  
Vol 111 (2) ◽  
pp. 790-862 ◽  
Author(s):  
Milan Randić ◽  
Jure Zupan ◽  
Alexandru T. Balaban ◽  
Dražen Vikić-Topić ◽  
Dejan Plavšić
Keyword(s):  
Graphical Representation ◽  
Graphical Representation Of Proteins

Graphical representation of proteins as four-color maps and their numerical characterization

2009 ◽  
Vol 27 (5) ◽  
pp. 637-641 ◽  
Author(s):  
Milan Randić ◽  
Ketij Mehulić ◽  
Damir Vukičević ◽  
Tomaž Pisanski ◽  
Dražen Vikić-Topić ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Numerical Characterization ◽  
Graphical Representation Of Proteins

Novel 2-D graphical representation of proteins

2006 ◽  
Vol 419 (4-6) ◽  
pp. 528-532 ◽  
Author(s):  
Milan Randić ◽  
Darko Butina ◽  
Jure Zupan
Keyword(s):  
Graphical Representation ◽  
Graphical Representation Of Proteins

Cp‐curve, a Novel 3‐D Graphical Representation of Proteins

2007 ◽  
Author(s):  
Haihua Bai ◽  
Chun Li ◽  
Hasi Agula ◽  
Jirimutu ◽  
Jun Wang ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Graphical Representation Of Proteins

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

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