González Díaz, HumbertoCruz Monteagudo, MaykelBorges, FernandaUriarte Villares, Eugenio2021-08-182021-08-182007González-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-11-013713-906980-19-7http://hdl.handle.net/10347/26828The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryBlood 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 researcheng© 2007 The author(s). Published by MDPI, Basel, Switzerland. Open AccessToxicoproteomicsDrug-induced cardiac toxicitiesMass spectrometryMass Spectrum graphMarkov modelQuantitative Proteome-Toxicity RelationshipsComplex NetworksPrincipal Components AnalysisPartial Orderbook part10.3390/ecsoc-11-01371open access