Sequential Superparamagnetic Clustering for Unbiased Classification of High-Dimensional Chemical Data

For the clustering of chemical structures that are described by the Similog, ISIS count, and ISIS binary fingerprints, we propose a sequential superparamagnetic clustering approach. To appropriately handle nonbinary feature keys, we introduce an extension of the binary Tanimoto similarity measure. I...

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Veröffentlicht in:	Journal of Chemical Information and Computer Sciences 2004-07, Vol.44 (4), p.1358-1364
Hauptverfasser:	Ott, Thomas, Kern, Albert, Schuffenhauer, Ausgar, Popov, Maxim, Acklin, Pierre, Jacoby, Edgar, Stoop, Ruedi
Format:	Artikel
Sprache:	eng
Schlagworte:	Binary system Chemicals Classification Computer science Magnetism
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creator	Ott, Thomas Kern, Albert Schuffenhauer, Ausgar Popov, Maxim Acklin, Pierre Jacoby, Edgar Stoop, Ruedi
description	For the clustering of chemical structures that are described by the Similog, ISIS count, and ISIS binary fingerprints, we propose a sequential superparamagnetic clustering approach. To appropriately handle nonbinary feature keys, we introduce an extension of the binary Tanimoto similarity measure. In our applications, data sets composed of structures from seven chemically distinct compound classes are evaluated and correctly clustered. The comparison, with results from leading methods, indicates the superiority of our sequential superparamagnetic clustering approach.
doi_str_mv	10.1021/ci049905c
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subjects	Binary system Chemicals Classification Computer science Magnetism
title	Sequential Superparamagnetic Clustering for Unbiased Classification of High-Dimensional Chemical Data
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