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Interactive Data Mining for Molecular Graphs

Designing new medical drugs for a specific disease requires extensive analysis of many molecules that have an activity for the disease. The main goal of these extensive analyses is to discover substructures (fragments) that account for the activity of these molecules. Once they are discovered, these...

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Detalles Bibliográficos
Autores principales: Yılmaz, Burcu, Göktürk, Mehmet
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801005/
https://www.ncbi.nlm.nih.gov/pubmed/20052387
http://dx.doi.org/10.1155/2009/502527
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author Yılmaz, Burcu
Göktürk, Mehmet
author_facet Yılmaz, Burcu
Göktürk, Mehmet
author_sort Yılmaz, Burcu
collection PubMed
description Designing new medical drugs for a specific disease requires extensive analysis of many molecules that have an activity for the disease. The main goal of these extensive analyses is to discover substructures (fragments) that account for the activity of these molecules. Once they are discovered, these fragments are used to understand the structure of new drugs and design new medicines for the disease. In this paper, we propose an interactive approach for visual molecule mining to discover fragments of molecules that are responsible for the desired activity with respect to a specific disease. Our approach visualizes molecular data in a form that can be interpreted by a human expert. Using a pipelining structure, it enables experts to contribute to the solution with their expertise at different levels. In order to derive desired fragments, it combines histogram-based filtering and clustering methods in a novel way. This combination enables a flexible determination of frequent fragments that repeat in molecules exactly or with some variations.
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spelling pubmed-28010052010-01-05 Interactive Data Mining for Molecular Graphs Yılmaz, Burcu Göktürk, Mehmet J Autom Methods Manag Chem Research Article Designing new medical drugs for a specific disease requires extensive analysis of many molecules that have an activity for the disease. The main goal of these extensive analyses is to discover substructures (fragments) that account for the activity of these molecules. Once they are discovered, these fragments are used to understand the structure of new drugs and design new medicines for the disease. In this paper, we propose an interactive approach for visual molecule mining to discover fragments of molecules that are responsible for the desired activity with respect to a specific disease. Our approach visualizes molecular data in a form that can be interpreted by a human expert. Using a pipelining structure, it enables experts to contribute to the solution with their expertise at different levels. In order to derive desired fragments, it combines histogram-based filtering and clustering methods in a novel way. This combination enables a flexible determination of frequent fragments that repeat in molecules exactly or with some variations. Hindawi Publishing Corporation 2009 2009-12-20 /pmc/articles/PMC2801005/ /pubmed/20052387 http://dx.doi.org/10.1155/2009/502527 Text en Copyright © 2009 B. Yılmaz and M. Göktürk. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yılmaz, Burcu
Göktürk, Mehmet
Interactive Data Mining for Molecular Graphs
title Interactive Data Mining for Molecular Graphs
title_full Interactive Data Mining for Molecular Graphs
title_fullStr Interactive Data Mining for Molecular Graphs
title_full_unstemmed Interactive Data Mining for Molecular Graphs
title_short Interactive Data Mining for Molecular Graphs
title_sort interactive data mining for molecular graphs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801005/
https://www.ncbi.nlm.nih.gov/pubmed/20052387
http://dx.doi.org/10.1155/2009/502527
work_keys_str_mv AT yılmazburcu interactivedataminingformoleculargraphs
AT gokturkmehmet interactivedataminingformoleculargraphs