TY  - DATA
AU  - Racz, Anita
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Héberger, Karoly
PY  - 2018
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3041
PB  - Springer, New York
T2  - Metabolomics
T1  - Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3041
ER  - 

@misc{
author = "Racz, Anita and Andrić, Filip and Bajusz, David and Héberger, Karoly",
year = "2018",
publisher = "Springer, New York",
journal = "Metabolomics",
title = "Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3041"
}

Racz, A., Andrić, F., Bajusz, D.,& Héberger, K.. (2018). Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y. in Metabolomics
Springer, New York..
https://hdl.handle.net/21.15107/rcub_cherry_3041

Racz A, Andrić F, Bajusz D, Héberger K. Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y. in Metabolomics. 2018;.
https://hdl.handle.net/21.15107/rcub_cherry_3041 .

Racz, Anita, Andrić, Filip, Bajusz, David, Héberger, Karoly, "Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y" in Metabolomics (2018),
https://hdl.handle.net/21.15107/rcub_cherry_3041 .

TY  - JOUR
AU  - Racz, Anita
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Héberger, Karoly
PY  - 2018
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/2101
AB  - Introduction Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. Objectives The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Methods Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Results Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Conclusion Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.
PB  - Springer, New York
T2  - Metabolomics
T1  - Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
VL  - 14
IS  - 3
DO  - 10.1007/s11306-018-1327-y
ER  - 

@article{
author = "Racz, Anita and Andrić, Filip and Bajusz, David and Héberger, Karoly",
year = "2018",
abstract = "Introduction Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. Objectives The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Methods Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Results Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Conclusion Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.",
publisher = "Springer, New York",
journal = "Metabolomics",
title = "Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles",
volume = "14",
number = "3",
doi = "10.1007/s11306-018-1327-y"
}

Racz, A., Andrić, F., Bajusz, D.,& Héberger, K.. (2018). Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles. in Metabolomics
Springer, New York., 14(3).
https://doi.org/10.1007/s11306-018-1327-y

Racz A, Andrić F, Bajusz D, Héberger K. Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles. in Metabolomics. 2018;14(3).
doi:10.1007/s11306-018-1327-y .

Racz, Anita, Andrić, Filip, Bajusz, David, Héberger, Karoly, "Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles" in Metabolomics, 14, no. 3 (2018),
https://doi.org/10.1007/s11306-018-1327-y . .