Tissue specific electrochemical fingerprinting

• Published in: PloS one 2012-11, Vol.7 (11), p.e49654
• Main Authors: Sobrova, Pavlina, Vyslouzilova, Lenka, Stepankova, Olga, Ryvolova, Marketa, Anyz, Jiri, Trnkova, Libuse, Adam, Vojtech, Hubalek, Jaromir, Kizek, Rene
• Format: Article
• Language: English
• Subjects: Agronomy; Analysis; Animal models; Animals; Biochemistry; Bioinformatics; Biology; Brain; Cancer; Cell cycle; Chemistry; Classification; Computational Biology - methods; Computer Science; Cybernetics; Data analysis; Data Interpretation, Statistical; Data mining; Data processing; Datasets; Decision analysis; Decision Trees; Electrical engineering; Electrochemical analysis; Electrochemistry; Electrochemistry - methods; Eye; Femur; Fingerprinting; Gene expression; Heart; Heat shock proteins; Information management; Kidneys; Knowledge discovery; Liver; Male; Mathematical models; Medical diagnosis; Metallothionein; Metallothionein - chemistry; Models, Statistical; Models, Theoretical; Muscles; Peptide mapping; Physiology; Proteomics; Proteomics - methods; Rabbits; Rats; Rats, Wistar; Research Design; Spleen; Thermal analysis; Tissue Distribution; Tissues; Voltammetry; Wavelet transforms
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0049654

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. 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. 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.