Artificial Neural Networks for Classification in Metabolomic
Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance

J 2011

Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance

BROUGHAM, D F; G. IVANOVA; M. GOTTSCHALK; D M COLLINS; A J EUSTACE et al.

Základní údaje

Originální název

Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance

Autoři

BROUGHAM, D F; G. IVANOVA; M. GOTTSCHALK; D M COLLINS; A J EUSTACE; R. O CONNOR a Josef HAVEL

Vydání

Journal of Biomedicine and Biotechnology, USA, New York, Hindawi Pub. Corp. 2011, 1110-7243

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10406 Analytical chemistry

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.436

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/11:00081755

Organizační jednotka

Přírodovědecká fakulta

DOI

https://doi.org/10.1155/2011/158094

UT WoS

000283224200001

Klíčová slova anglicky

NMR SPECTROSCOPIC DATA; HUMAN BRAIN-TUMORS; H-1-NMR SPECTROSCOPY; PATTERN-RECOGNITION; IN-VITRO; EXTRACTS; IDENTIFICATION; LINES; VIVO; RESISTANCE

Štítky

AKR, rivok

Změněno: 28. 4. 2016 12:48, Ing. Andrea Mikešková

Anotace

V originále

We report the successful classification, by artificial neural networks (ANNs), of H-1 NMR spectroscopic data recorded on whole-cell culture samples of four different lung carcinoma cell lines, which display different drug resistance patterns. The robustness of the approach was demonstrated by its ability to classify the cell line correctly in 100% of cases, despite the demonstrated presence of operator-induced sources of variation, and irrespective of which spectra are used for training and for validation. The study demonstrates the potential of ANN for lung carcinoma classification in realistic situations.

Návaznosti

LC06035, projekt VaV

Název: Centrum biofyzikální chemie, bioelektrochemie a bioanalýzy. Nové nástroje pro genomiku, proteomiku a biomedicínu.

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum biofyzikální chemie, bioelektrochemie a bioanalýzy. Nové nástroje pro genomiku, proteomiku a biomedicínu

Citovat

BROUGHAM, D F; G. IVANOVA; M. GOTTSCHALK; D M COLLINS; A J EUSTACE; R. O CONNOR a Josef HAVEL. Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance. Journal of Biomedicine and Biotechnology. USA, New York: Hindawi Pub. Corp., 2011, "Neuveden", JANUARY, s. "nestrankovano", 8 s. ISSN 1110-7243. Dostupné z: https://doi.org/10.1155/2011/158094.

@article{918612,
   author = {Brougham, D F and Ivanova, G. and Gottschalk, M. and Collins, D M and Eustace, A J and O Connor, R. and Havel, Josef},
   article_location = {USA, New York},
   article_number = {JANUARY},
   doi = {https://doi.org/10.1155/2011/158094},
   keywords = {NMR SPECTROSCOPIC DATA; HUMAN BRAIN-TUMORS; H-1-NMR SPECTROSCOPY; PATTERN-RECOGNITION; IN-VITRO; EXTRACTS; IDENTIFICATION; LINES; VIVO; RESISTANCE},
   language = {eng},
   issn = {1110-7243},
   journal = {Journal of Biomedicine and Biotechnology},
   title = {Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance},
   volume = {"Neuveden"},
   year = {2011}
}

TY  - JOUR
ID  - 918612
AU  - Brougham, D F - Ivanova, G. - Gottschalk, M. - Collins, D M - Eustace, A J - O Connor, R. - Havel, Josef
PY  - 2011
TI  - Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance
JF  - Journal of Biomedicine and Biotechnology
VL  - "Neuveden"
IS  - JANUARY
SP  - "nestrankovano"
EP  - "nestrankovano"
PB  - Hindawi Pub. Corp.
SN  - 11107243
KW  - NMR SPECTROSCOPIC DATA
KW  - HUMAN BRAIN-TUMORS
KW  - H-1-NMR SPECTROSCOPY
KW  - PATTERN-RECOGNITION
KW  - IN-VITRO
KW  - EXTRACTS
KW  - IDENTIFICATION
KW  - LINES
KW  - VIVO
KW  - RESISTANCE
N2  - We report the successful classification, by artificial neural networks (ANNs), of H-1 NMR spectroscopic data recorded on whole-cell culture samples of four different lung carcinoma cell lines, which display different drug resistance patterns. The robustness of the approach was demonstrated by its ability to classify the cell line correctly in 100% of cases, despite the demonstrated presence of operator-induced sources of variation, and irrespective of which spectra are used for training and for validation. The study demonstrates the potential of ANN for lung carcinoma classification in realistic situations.
ER  -

BROUGHAM, D F; G. IVANOVA; M. GOTTSCHALK; D M COLLINS; A J EUSTACE; R. O CONNOR a Josef HAVEL. Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using H-1 Nuclear Magnetic Resonance. \textit{Journal of Biomedicine and Biotechnology}. USA, New York: Hindawi Pub. Corp., 2011, ''Neuveden'', JANUARY, s.~''nestrankovano'', 8 s. ISSN~1110-7243. Dostupné z: https://doi.org/10.1155/2011/158094.

Přehled o publikaci