12 0

Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials

Title
Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials
Author
윤태현
Keywords
Nanomaterials; Descriptors selection; Principal component analysis; Toxicity prediction
Issue Date
2018-06
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
COMPUTERS IN BIOLOGY AND MEDICINE, v. 99, page. 161-172
Abstract
The present study is based on the application of a multivariate statistical analysis approach for the selection of optimal descriptors of nanomaterials with the objective of robust qualitative modeling of their toxicity. A novel data mining protocol has been developed for the selection of an optimal subset of descriptors of nanomaterials by using the well-known multivariate method principal component analysis (PCA). The selected subsets of descriptors were validated for qualitative modeling of the toxicity of nanomaterials in the PC space. The analysis and validation of the proposed schemes were based on five decisive nanomaterial toxicity data sets available in the published literature. Optimal descriptors were selected on the basis of the maximum loading criteria and using a threshold value of cumulative variance <= 90% on PC directions. A maximum inter-class separation(B) and the minimum intra-classes separation(A) were obtained for toxic vs. nontoxic nanomaterials in the PC space with the selected subsets of optimal descriptors compared to their other combinations for each of the datasets.
URI
https://www.sciencedirect.com/science/article/pii/S0010482518301604?via%3Dihubhttp://repository.hanyang.ac.kr/handle/20.500.11754/119036
ISSN
0010-4825; 1879-0534
DOI
10.1016/j.compbiomed.2018.06.012
Appears in Collections:
COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > CHEMISTRY(화학과) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE