Nonlinear N - Compartments model of respiratory mechanics considering viscoelasticity, inertia and surface tension properties.

Respiratory biomechanics constitutes an important topic in clinical practice. Different strategies like mathematical models have been implemented to understand and replicate scenarios allowing deeper analysis. In this paper, a nonlinear N - compartments model is presented, allowing to represent the lung in a heterogeneous way. It considers the resistance of each generation of the airway and each alveolar compartment characterized independently. Includes properties of nonlinear elastance, viscoelasticity, inertia, and surface tension. In this work, to show the functionality of the model, a simulation of four alveolar units coupled to the airway model is presented using pressure as input signal simulating mechanical ventilation. However, the model can be used to simulate any desired number of alveolar units. Values at airway output were compared to the linear model, obtaining a correlation close to 1. Also, was compared to a physical test lung using Hamilton - S1 mechanical ventilator obtaining a positive correlation. The model makes it possible to evaluate the effects of different properties during spontaneous respiration or mechanical ventilation, both at the airway opening and alveolar. These properties include viscoelasticity, surface tension, inertia, among others.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Multi-classifier based on hard instances- new method for prediction of human immunodeficiency virus drug resistance.

There are several classification problems, which are difficult to solve using a single classifier because of the complexity of the decision boundary. Whereas a wide variety of multiple classifier systems have been built with the purpose of improving the recognition process, there is no universal method performing the best. This paper provides a review of different multi-classifiers and some application of them. Also it is shown a novel model of combining classifiers and its application to predicting human immunodeficiency virus drug resistance from genotype. The proposal is based on the use of different classifier models. It clusters the dataset considering the performance of the base classifiers. The system learns how to decide from the groups, by using a meta-classifier, which are the best classifiers for a given pattern. The proposed model is compared with well-known classifier ensembles and individual classifiers as well resulting the novel model in similar or even better performance.

ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds.

Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.