Ordered Weighted Average Based grouping of nanomaterials with Arsinh and Dose Response similarity models

TL;DR

This paper introduces a novel, dataset-independent method for grouping nanomaterials using advanced similarity metrics and hierarchical clustering, with a supporting R software tool, applied to Daphnia magna immobilization data.

Contribution

The paper presents a new similarity assessment and grouping methodology for nanomaterials based on Arsinh transformation, shape comparison, and OWA aggregation, implemented in R.

Findings

Method effectively groups nanomaterials regardless of dataset changes

Software tool facilitates application of the methodology

Test case demonstrates practical utility with nanomaterials data

Abstract

In the context of the EU GRACIOUS project, we propose a novel procedure for similarity assessment and grouping of nanomaterials. This methodology is based on the (1) Arsinh transformation function for scalar properties, (2) full curve shape comparison by application of a modified Kolmogorov-Smirnov metric for bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based grouping distance, and (4) hierarchical clustering. The approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. To facilitate the application of the proposed methodology, a software script was developed by using the R programming language which is currently under migration to a web tool. The presented approach was tested against a dataset, derived from literature review, related to immobilisation of Daphnia magna and reporting information on several nanomaterials.