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A molecular structure matching approach to efficient identification of endogenous mammalian biochemical structures.

Hamdalla MA, Ammar RA, Rajasekaran S - BMC Bioinformatics (2015)

Bottom Line: It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression.The results of a comprehensive set of empirical experiments suggest that BioSMXpress identifies endogenous mammalian biochemical structures with high accuracy.BioSMXpress is 8 times faster than our previous work BioSM without compromising the accuracy of the predictions made.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Metabolomics is the study of small molecules, called metabolites, of a cell, tissue or organism. It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression. A major challenge in metabolomics research is the structural identification of unknown biochemical compounds in complex biofluids. In this paper we present an efficient cheminformatics tool, BioSMXpress that uses known endogenous mammalian biochemicals and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSMXpress identifies endogenous mammalian biochemical structures with high accuracy. BioSMXpress is 8 times faster than our previous work BioSM without compromising the accuracy of the predictions made. BioSMXpress is freely available at http://engr.uconn.edu/~rajasek/BioSMXpress.zip.

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Biological predictions resulting from a set of LOOCV experiments by BioSMXpress and BioSM with 1,387 KEGG compounds. Compounds were binned by atom count.
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Related In: Results  -  Collection

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Figure 2: Biological predictions resulting from a set of LOOCV experiments by BioSMXpress and BioSM with 1,387 KEGG compounds. Compounds were binned by atom count.

Mentions: Using 1,387 scaffolds in a set of LOOCV experiments implemented by BioSMXpress and BioSM independently were implemented and compared. These 1,387 compounds were the scaffolds that satisfied the constraints of both BioSM and BioSMXpress (mass in the range of 50 - 700 Da and number of atoms in the range of 4 - 53). BioSM was capable of identifying 94.5% of the 1,387 scaffolds as biochemical structures while BioSMXpress identified 94.2%. Figure 2 shows the breakdown of the results of this comparison with compounds binned by atom count.


A molecular structure matching approach to efficient identification of endogenous mammalian biochemical structures.

Hamdalla MA, Ammar RA, Rajasekaran S - BMC Bioinformatics (2015)

Biological predictions resulting from a set of LOOCV experiments by BioSMXpress and BioSM with 1,387 KEGG compounds. Compounds were binned by atom count.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4402589&req=5

Figure 2: Biological predictions resulting from a set of LOOCV experiments by BioSMXpress and BioSM with 1,387 KEGG compounds. Compounds were binned by atom count.
Mentions: Using 1,387 scaffolds in a set of LOOCV experiments implemented by BioSMXpress and BioSM independently were implemented and compared. These 1,387 compounds were the scaffolds that satisfied the constraints of both BioSM and BioSMXpress (mass in the range of 50 - 700 Da and number of atoms in the range of 4 - 53). BioSM was capable of identifying 94.5% of the 1,387 scaffolds as biochemical structures while BioSMXpress identified 94.2%. Figure 2 shows the breakdown of the results of this comparison with compounds binned by atom count.

Bottom Line: It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression.The results of a comprehensive set of empirical experiments suggest that BioSMXpress identifies endogenous mammalian biochemical structures with high accuracy.BioSMXpress is 8 times faster than our previous work BioSM without compromising the accuracy of the predictions made.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Metabolomics is the study of small molecules, called metabolites, of a cell, tissue or organism. It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression. A major challenge in metabolomics research is the structural identification of unknown biochemical compounds in complex biofluids. In this paper we present an efficient cheminformatics tool, BioSMXpress that uses known endogenous mammalian biochemicals and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSMXpress identifies endogenous mammalian biochemical structures with high accuracy. BioSMXpress is 8 times faster than our previous work BioSM without compromising the accuracy of the predictions made. BioSMXpress is freely available at http://engr.uconn.edu/~rajasek/BioSMXpress.zip.

Show MeSH