Digital Knowledge Repository of Central Drug Research Institute, Lucknow (India) >
Publications of CDRI Scientists >
Medicinal and Process Chemistry >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/81
|
| Title: | A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study. |
| Authors: | Prabhakar, Yenamandra S |
| Keywords: | Regression analysis
variable selection
combinatorial approach
antimycin A1 analogues
antifilarial |
| Issue Date: | 2003 |
| Citation: | QSAR & Combinatorial Chemistry Science (2003), 22, 538 |
| Series/Report no.: | CDRI Communication Number 6225 |
| Abstract: | A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time. |
| URI: | http://hdl.handle.net/123456789/81 |
| Appears in Collections: | Medicinal and Process Chemistry
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| YSP-6225-QSAR_Comb_Sci_2003_22_538.pdf | | 352Kb | Adobe PDF | View/Open |
|
All items in DSpace are protected by copyright, with all rights reserved.
|
