Nonlinear dimensionality reduction for visualizing toxicity data: distance-based versus topology-based approaches.

Abstract:

:Over the years, a number of dimensionality reduction techniques have been proposed and used in chemoinformatics to perform nonlinear mappings. In this study, four representatives of nonlinear dimensionality reduction methods related to two different families were analyzed: distance-based approaches (Isomap and Diffusion Maps) and topology-based approaches (Generative Topographic Mapping (GTM) and Laplacian Eigenmaps). The considered methods were applied for the visualization of three toxicity datasets by using four sets of descriptors. Two methods, GTM and Diffusion Maps, were identified as the best approaches, which thus made it impossible to prioritize a single family of the considered dimensionality reduction methods. The intrinsic dimensionality assessment of data was performed by using the Maximum Likelihood Estimation. It was observed that descriptor sets with a higher intrinsic dimensionality contributed maps of lower quality. A new statistical coefficient, which combines two previously known ones, was proposed to automatically rank the maps. Instead of relying on one of the best methods, we propose to automatically generate maps with different parameter values for different descriptor sets. By following this procedure, the maps with the highest values of the introduced statistical coefficient can be automatically selected and used as a starting point for visual inspection by the user.

journal_name

ChemMedChem

journal_title

ChemMedChem

authors

Kireeva NV,Ovchinnikova SI,Tetko IV,Asiri AM,Balakin KV,Tsivadze AY

doi

10.1002/cmdc.201400027

subject

Has Abstract

pub_date

2014-05-01 00:00:00

pages

1047-59

issue

5

eissn

1860-7179

issn

1860-7187

journal_volume

9

pub_type

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