Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation. / Spicker, Jeppe; Brunak, Søren; Frederiksen, Klaus; Toft, Henrik.

In: Toxicological sciences : an official journal of the Society of Toxicology, Vol. 102, No. 2, 2008, p. 444-54.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Spicker, J, Brunak, S, Frederiksen, K & Toft, H 2008, 'Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation', Toxicological sciences : an official journal of the Society of Toxicology, vol. 102, no. 2, pp. 444-54. https://doi.org/10.1093/toxsci/kfn001

APA

Spicker, J., Brunak, S., Frederiksen, K., & Toft, H. (2008). Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation. Toxicological sciences : an official journal of the Society of Toxicology, 102(2), 444-54. https://doi.org/10.1093/toxsci/kfn001

Vancouver

Spicker J, Brunak S, Frederiksen K, Toft H. Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation. Toxicological sciences : an official journal of the Society of Toxicology. 2008;102(2):444-54. https://doi.org/10.1093/toxsci/kfn001

Author

Spicker, Jeppe ; Brunak, Søren ; Frederiksen, Klaus ; Toft, Henrik. / Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation. In: Toxicological sciences : an official journal of the Society of Toxicology. 2008 ; Vol. 102, No. 2. pp. 444-54.

Bibtex

@article{0204a44a35f54790a75a0ed9462ee87a,
title = "Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation",
abstract = "A large number of databases are currently being implemented within toxicology aiming to integrate diverse biological data, such as clinical chemistry, expression, and other types of data. However, for these endeavors to be successful, tools for integration, visualization, and interpretation are needed. This paper presents a method for data integration using a hierarchical model based on either principal component analysis or partial least squares discriminant analysis of clinical chemistry, expression, and nuclear magnetic resonance data using a toxicological study as case. The study includes the three toxicants alpha-naphthyl-isothiocyanate, dimethylnitrosamine, and N-methylformamide administered to rats. Improved predictive ability of the different classes is seen, suggesting that this approach is a suitable method for data integration and visualization of biological data. Furthermore, the method allows for correlation of biological parameters between the different data types, which could lead to an improvement in biological interpretation.",
author = "Jeppe Spicker and S{\o}ren Brunak and Klaus Frederiksen and Henrik Toft",
year = "2008",
doi = "10.1093/toxsci/kfn001",
language = "English",
volume = "102",
pages = "444--54",
journal = "Toxicological Sciences",
issn = "1096-6080",
publisher = "Oxford University Press",
number = "2",

}

RIS

TY - JOUR

T1 - Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation

AU - Spicker, Jeppe

AU - Brunak, Søren

AU - Frederiksen, Klaus

AU - Toft, Henrik

PY - 2008

Y1 - 2008

N2 - A large number of databases are currently being implemented within toxicology aiming to integrate diverse biological data, such as clinical chemistry, expression, and other types of data. However, for these endeavors to be successful, tools for integration, visualization, and interpretation are needed. This paper presents a method for data integration using a hierarchical model based on either principal component analysis or partial least squares discriminant analysis of clinical chemistry, expression, and nuclear magnetic resonance data using a toxicological study as case. The study includes the three toxicants alpha-naphthyl-isothiocyanate, dimethylnitrosamine, and N-methylformamide administered to rats. Improved predictive ability of the different classes is seen, suggesting that this approach is a suitable method for data integration and visualization of biological data. Furthermore, the method allows for correlation of biological parameters between the different data types, which could lead to an improvement in biological interpretation.

AB - A large number of databases are currently being implemented within toxicology aiming to integrate diverse biological data, such as clinical chemistry, expression, and other types of data. However, for these endeavors to be successful, tools for integration, visualization, and interpretation are needed. This paper presents a method for data integration using a hierarchical model based on either principal component analysis or partial least squares discriminant analysis of clinical chemistry, expression, and nuclear magnetic resonance data using a toxicological study as case. The study includes the three toxicants alpha-naphthyl-isothiocyanate, dimethylnitrosamine, and N-methylformamide administered to rats. Improved predictive ability of the different classes is seen, suggesting that this approach is a suitable method for data integration and visualization of biological data. Furthermore, the method allows for correlation of biological parameters between the different data types, which could lead to an improvement in biological interpretation.

U2 - 10.1093/toxsci/kfn001

DO - 10.1093/toxsci/kfn001

M3 - Journal article

C2 - 18178960

VL - 102

SP - 444

EP - 454

JO - Toxicological Sciences

JF - Toxicological Sciences

SN - 1096-6080

IS - 2

ER -

ID: 40804600