Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data from cardiac surgery patients: a prospective observational study.

BACKGROUND: Warfarin is a common oral anticoagulant, and its effects vary widely among individuals. Numerous dose-prediction algorithms have been reported based on cross-sectional data generated via multiple linear regression or machine learning. This study aimed to construct an information fusion perturbation theory and machine learning prediction model of warfarin blood levels based on clinical longitudinal data from cardiac surgery patients. METHODS AND MATERIAL: The data of 246 patients were obtained from electronic medical records. Continuous variables were processed by calculating the distance of the raw data with the moving average (MA ∆vki(sj)), and categorical variables in different attribute groups were processed using Euclidean distance (ED ǁ∆vk(sj)ǁ). Regression and classification analyses were performed on the raw data, MA ∆vki(sj), and ED ǁ∆vk(sj)ǁ. Different machine-learning algorithms were chosen for the STATISTICA and WEKA software. RESULTS: The random forest (RF) algorithm was the best for predicting continuous outputs using the raw data. The correlation coefficients of the RF algorithm were 0.978 and 0.595 for the training and validation sets, respectively, and the mean absolute errors were 0.135 and 0.362 for the training and validation sets, respectively. The proportion of ideal predictions of the RF algorithm was 59.0%. General discriminant analysis (GDA) was the best algorithm for predicting the categorical outputs using the MA ∆vki(sj) data. The GDA algorithm's total true positive rate (TPR) was 95.4% and 95.6% for the training and validation sets, respectively, with MA ∆vki(sj) data. CONCLUSIONS: An information fusion perturbation theory and machine learning model for predicting warfarin blood levels was established. A model based on the RF algorithm could be used to predict the target international normalized ratio (INR), and a model based on the GDA algorithm could be used to predict the probability of being within the target INR range under different clinical scenarios.

OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues.

Familial hypercholesterolemia (FH) is an inherited metabolic disease affecting cholesterol metabolism, with 90% of cases caused by mutations in the LDL receptor gene (LDLR), primarily missense mutations. This study aims to integrate six commonly used predictive software to create a new model for predicting LDLR mutation pathogenicity and mapping hot spot residues. Six predictive-software are selected: Polyphen-2, SIFT, MutationTaster, REVEL, VARITY, and MLb-LDLr. Software accuracy is tested with the characterized variants annotated in ClinVar and, by bioinformatic and machine learning techniques all models are integrated into a more accurate one. The resulting optimized model presents a specificity of 96.71% and a sensitivity of 98.36%. Hot spot residues with high potential of pathogenicity appear across all domains except for the signal peptide and the O-linked domain. In addition, translating this information into 3D structure of the LDLr highlights potentially pathogenic clusters within the different domains, which may be related to specific biological function. The results of this work provide a powerful tool to classify LDLR pathogenic variants. Moreover, an open-access guide user interface (OptiMo-LDLr) is provided to the scientific community. This study shows that combination of several predictive software results in a more accurate prediction to help clinicians in FH diagnosis.

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches.

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R2 (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing.

Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60-70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine.

Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp ≈ Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis.

Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.

Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants.

The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a star graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and perturbation theory operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as multiple linear regression (LM), partial least squares regression (PLS), neural networks regression (NN), and random forest (RF). RF provides the best models to predict the mitochondrial oxygen consumption in the presence of specific CNTs with R2 of 0.998-0.999 and RMSE of 0.0068-0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalograms in epilepsy and also as a prospective chemoinformatics tool for nanorisk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349 .

Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins.

The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against 22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets.

Experimental and computational studies of fatty acid distribution networks.

Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal microbiome fatty acid (FA) composition/distribution play a vital role in discovering the contribution of food components to ruminant products (e.g., meat and milk) when pursuing a healthy diet. Hansch's models based on Linear Free Energy Relationships (LFERs) using physicochemical parameters, such as partition coefficients, molar refractivity, and polarizability, as input variables (Vk) are advocated. In this work, a new combined experimental and theoretical strategy was proposed to study the effect of ω-6/ω-3 ratios, FA chemical structure, and other factors over FA distribution networks in the ruminal microbiome. In step 1, experiments were carried out to measure long chain fatty acid (LCFA) profiles in the rumen microbiome (bacterial and protozoan), and volatile fatty acids (VFAs) in fermentation media. In step 2, the proportions and physicochemical parameter values of LCFAs and VFAs were calculated under different boundary conditions (cj) like c1 = acid and/or base methylation treatments, c2 = with/without fermentation, c3 = FA distribution phase (media, bacterial, or protozoan microbiome), etc. In step 3, Perturbation Theory (PT) and LFER ideas were combined to develop a PT-LFER model of a FA distribution network using physicochemical parameters (V(k)), the corresponding Box-Jenkins (ΔV(kj)) and PT operators (ΔΔV(kj)) in statistical analysis. The best PT-LFER model found predicted the effects of perturbations over the FA distribution network with sensitivity, specificity, and accuracy > 80% for 407 655 cases in training + external validation series. In step 4, alternative PT-LFER and PT-NLFER models were tested for training Linear and Non-Linear Artificial Neural Networks (ANNs). PT-NLFER models based on ANNs presented better performance but are more complicated than the PT-LFER model. Last, in step 5, the PT-LFER model based on LDA was used to reconstruct the complex networks of perturbations in the FA distribution and compared the giant components of the observed and predicted networks with random Erdos-Rényi network models. In short, our new PT-LFER model is a useful tool for predicting a distribution network in terms of specific fatty acid distribution.

Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway.

The ubiquitin-proteasome pathway (UPP) plays an important role in the degradation of cellular proteins and regulation of different cellular processes that include cell cycle control, proliferation, differentiation, and apoptosis. In this sense, the disruption of proteasome activity leads to different pathological states linked to clinical disorders such as inflammation, neurodegeneration, and cancer. The use of UPP inhibitors is one of the proposed approaches to manage these alterations. On other hand, the ChEMBL database contains >5,000 experimental outcomes for >2,000 compounds tested as possible proteasome inhibitors using a large number of pharmacological assay protocols. All these assays report a large number of experimental parameters of biological activity like EC50, IC50 percent of inhibition, and many others that have been determined under many different conditions, targets, organisms, etc. Although this large amount of data offers new opportunities for the computational discovery of proteasome inhibitors, the complexity of these data represents a bottleneck for the development of predictive models. In this work, we used linear molecular indices calculated with the software TOMOCOMD-CARDD and Box-Jenkins moving average operators to develop a multi-output model that can predict outcomes for 20 experimental parameters in >450 assays carried out under different conditions. This generated multi-output model showed values of accuracy, sensitivity, and specificity above 70% for training and validation series. Finally, this model is considered multi-target and multi-scale, because it predicts the inhibition of the UPP for drugs against 22 molecular or cellular targets of different organisms contained in the ChEMBL database.

Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach.

Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.

Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms.

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks.

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.

Model for high-throughput screening of drug immunotoxicity--study of the anti-microbial G1 over peritoneal macrophages using flow cytometry.

Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 µg/mL and Cytotoxicity = 23.6% for the anti-microbial/anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). The results obtained complement the toxicological studies of this important drug. This work adds a new tool to the existing pool of few methods useful for multi-target HTS of ChEMBL and other libraries of compounds towards drug discovery.

LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design.

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

Immunotoxicity, flow cytometry, and chemoinformatics: review, bibliometric analysis, and new QSAR model of drug effects over macrophages.

Bibliometric methods for analyzing and describing research output have been supported internationally by the establishment and operation of organizations such as the Institute for Scientific Information (ISI) or Scimago Ranking Institutions (SRI). This study provides an overview of the research performance of major World countries in the field cytokines, Citometric bead assays and QSAR, the most important journals in which they published their research articles, and the most important academic institutions publishing them. The analysis was based on Thomson Scientific's Web of Science (WoS), and Scimago group calculated bibliometric indicators of publication activity and actual citation impact. Studying the time period 2005-2010, and shows the visibility of Medicinal Chemistry Bioorganic in this thematic noting that the visibility of a journal must take into account not only the impact factor, but the prestige, popularity and representativeness of the theme that addresses the same making a comprehensive assessment of bibliometric indicators.

ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen.

Multiplexed biological assays provide multiple measurements of cellular parameters in the same test. In this work, we have trained and tested an Artificial Neural Network (ANN) model for the first time, in order to perform a multiplexing prediction of drugs effect on macrophage populations. In so doing, we have used the TOPS-MODE approach to calculate drug molecular descriptors and the software STATISTICA to seek different ANN models such as: Linear Neural Network (LNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN) and Multi-Layer Perceptrons (MLP). The best model found was the LNN, which correctly classified 8258 out of 9000 (Accuracy = 93.0%) multiplexing assay endpoints of 7903 drugs (including both training and test series). Each endpoint corresponds to one out of 1418 assays, 36 molecular or cellular targets, 46 standard type measures, in two possible organisms (human and mouse). Secondly, we have determined experimentally, for the first time, the values of EC(50) = 11.41 µg/mL and Cytotoxicity = 27.1% for the drug G1 over Balb/C mouse spleen macrophages using flow cytometry. In addition, we have used the LNN model to predict the G1 activity in 1265 multiplexing assays not measured experimentally (including 152 cytotoxicity assay endpoints). Both experimental and theoretical results point out a low macrophage cytotoxicity of G1. This work breaks new ground for the 'in silico' multiplexing screening of large libraries of compounds. The results obtained are very significant because they complement the immunotoxicology studies of this important anti-microbial/anti-parasite drug.

Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer.

Fast cancer diagnosis represents a real necessity in applied medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because it permits us to numerically describe any real system such as the protein macromolecules by transforming real properties into molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naïve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.