RT Book,_Section
T1 Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs
A1 González Díaz, Humberto
A1 Cruz Monteagudo, Maykel
A1 Borges, Fernanda
A1 Uriarte Villares, Eugenio
K1 Toxicoproteomics
K1 Drug-induced cardiac toxicities
K1 Mass spectrometry
K1 Mass Spectrum graph
K1 Markov model
K1 Quantitative Proteome-Toxicity Relationships
K1 Complex Networks
K1 Principal Components Analysis
K1 Partial Order
AB Blood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nopk) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nopk values present in the QPTR model explains with one factor (F1) the 82.7% of variance. Next, these nopk values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome research
PB MDPI
SN 3-906980-19-7
YR 2007
FD 2007
LK http://hdl.handle.net/10347/26828
UL http://hdl.handle.net/10347/26828
LA eng
NO González-Díaz, H.; Cruz-Monteagudo, M.; Borges, F.; Uriarte, E. Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs, in Proceedings of the 11th International Electronic Conference on Synthetic Organic Chemistry, 1–30 November 2007, MDPI: Basel, Switzerland, doi:10.3390/ecsoc-11-01371
NO The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational Chemistry
NO The authors thank projects funded by the Xunta de Galicia (PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (PI061457). González-Díaz H. acknowledges tenure track research position funded by the Program Isidro Parga Pondal, Xunta de Galicia
DS Minerva
RD 28 abr 2026