Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤태현 | - |
dc.date.accessioned | 2019-12-08T08:48:19Z | - |
dc.date.available | 2019-12-08T08:48:19Z | - |
dc.date.issued | 2018-06 | - |
dc.identifier.citation | COMPUTERS IN BIOLOGY AND MEDICINE, v. 99, page. 161-172 | en_US |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.issn | 1879-0534 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0010482518301604?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/119036 | - |
dc.description.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. | en_US |
dc.description.sponsorship | This work is supported by The Startup Foundation for Introducing Talent of NUIST (grant number 2017r103) and the R &amp; D Program "Development of User-friendly Nano-safety Prediction System" (grant number 10043929) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.subject | Nanomaterials | en_US |
dc.subject | Descriptors selection | en_US |
dc.subject | Principal component analysis | en_US |
dc.subject | Toxicity prediction | en_US |
dc.title | Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials | en_US |
dc.type | Article | en_US |
dc.relation.volume | 99 | - |
dc.identifier.doi | 10.1016/j.compbiomed.2018.06.012 | - |
dc.relation.page | 161-172 | - |
dc.relation.journal | COMPUTERS IN BIOLOGY AND MEDICINE | - |
dc.contributor.googleauthor | Jha, Sunil Kr | - |
dc.contributor.googleauthor | Yoon, T. H. | - |
dc.contributor.googleauthor | Pan, Zhaoqing | - |
dc.relation.code | 2018002873 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF NATURAL SCIENCES[S] | - |
dc.sector.department | DEPARTMENT OF CHEMISTRY | - |
dc.identifier.pid | taeyoon | - |
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