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Statements

Subject Item
n2:RIV%2F62156489%3A43210%2F12%3A00199016%21RIV13-GA0-43210___
rdf:type
skos:Concept n11:Vysledek
dcterms:description
Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. Methodology/Principal Findings: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. Conclusions/Significance: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue. Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. Methodology/Principal Findings: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. Conclusions/Significance: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue.
dcterms:title
Tissue specific electrochemical fingerprinting Tissue specific electrochemical fingerprinting
skos:prefLabel
Tissue specific electrochemical fingerprinting Tissue specific electrochemical fingerprinting
skos:notation
RIV/62156489:43210/12:00199016!RIV13-GA0-43210___
n11:predkladatel
n12:orjk%3A43210
n4:aktivita
n7:Z n7:I n7:P
n4:aktivity
I, P(ED1.1.00/02.0068), P(GAP102/11/1068), Z(MSM6840770038)
n4:cisloPeriodika
11
n4:dodaniDat
n8:2013
n4:domaciTvurceVysledku
n14:3790185 n14:2307049 n14:7333323 n14:2276895 n14:2032279 n14:4995775 n14:4489705
n4:druhVysledku
n15:J
n4:duvernostUdaju
n5:S
n4:entitaPredkladatele
n9:predkladatel
n4:idSjednocenehoVysledku
174520
n4:idVysledku
RIV/62156489:43210/12:00199016
n4:jazykVysledku
n16:eng
n4:klicovaSlova
proteomics; interdisciplinary fields; metallothinein
n4:klicoveSlovo
n6:interdisciplinary%20fields n6:metallothinein n6:proteomics
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[6BB1A897DEDF]
n4:nazevZdroje
PLoS One
n4:obor
n18:CB
n4:pocetDomacichTvurcuVysledku
7
n4:pocetTvurcuVysledku
9
n4:projekt
n10:GAP102%2F11%2F1068 n10:ED1.1.00%2F02.0068
n4:rokUplatneniVysledku
n8:2012
n4:svazekPeriodika
7
n4:tvurceVysledku
Vysloužilová, Lenka Štěpánková, Olga Šobrová, Pavlína Hubálek, Jaromír Ryvolová, Markéta Trnková, Libuše Kizek, René Adam, Vojtěch Anyz, Jiří
n4:wos
311333800046
n4:zamer
n13:MSM6840770038
s:issn
1932-6203
s:numberOfPages
12
n21:doi
10.1371/journal.pone.0049654
n20:organizacniJednotka
43210