Name
Title | ||
|---|---|---|
RRegrs: an R package for computer-aided model selection with multiple regression models |
Abstract | ||
|---|---|---|
The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s13321-015-0094-2 | Journal of Cheminformatics |
Keywords | Field | DocType |
Caret-based tool,Multiple regression,QSAR,R package | Data mining,Regression analysis,Computer science,Computer-aided,Artificial intelligence,Predictive modelling,Standardization,Cheminformatics,Linear regression,Regression,Model selection,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 1 | 1758-2946 |
Citations | PageRank | References |
4 | 0.45 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgia Tsiliki | 1 | 33 | 4.85 |
| Cristian R. Munteanu | 2 | 100 | 10.27 |
| José A. Seoane | 3 | 76 | 9.29 |
| Carlos Fernandez-Lozano | 4 | 10 | 0.91 |
| Haralambos Sarimveis | 5 | 458 | 30.49 |
| E Willighagen | 6 | 551 | 37.27 |