Electron spin resonance as a powerful tool for studying antioxidants and radicals.

The study of antioxidants and radicals has always been a complex task due to the special characteristics of these species such as reactions at low concentrations and short half-lives. Current techniques do not always produce good results and in some cases they can only be applied in chemical models. From this point of view, the development of electron spin resonance (ESR) has allowed the study of the antioxidant capacity of a wide variety of compounds and the detection of radicals in the reactions in which they are involved. The DPPH technique allows only the study of antioxidants in pure chemical models. The ORAC-ESR assay, based on the spin trapping technique, emerges as an interesting tool for identifying and quantifying the antioxidant capacity of different samples. Furthermore, the spin trapping technique allows us to characterize radicals in in vivo/ex vivo models. The present review discusses the current available techniques associated with ESR for the study of antioxidants and radical species.

Machine learning and data mining: strategies for hypothesis generation.

Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an 'organic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.

Empirical risk minimization for support vector classifiers.

In this paper, we propose a general technique for solving support vector classifiers (SVCs) for an arbitrary loss function, relying on the application of an iterative reweighted least squares (IRWLS) procedure. We further show that three properties of the SVC solution can be written as conditions over the loss function. This technique allows the implementation of the empirical risk minimization (ERM) inductive principle on large margin classifiers obtaining, at the same time, very compact (in terms of number of support vectors) solutions. The improvements obtained by changing the SVC loss function are illustrated with synthetic and real data examples.

Weighted least squares training of support vector classifiers leading to compact and adaptive schemes.

An iterative block training method for support vector classifiers (SVCs) based on weighted least squares (WLS) optimization is presented. The algorithm, which minimizes structural risk in the primal space, is applicable to both linear and nonlinear machines. In some nonlinear cases, it is necessary to previously find a projection of data onto an intermediate-dimensional space by means of either principal component analysis or clustering techniques. The proposed approach yields very compact machines, the complexity reduction with respect to the SVC solution is especially notable in problems with highly overlapped classes. Furthermore, the formulation in terms of WLS minimization makes the development of adaptive SVCs straightforward, opening up new fields of application for this type of model, mainly online processing of large amounts of (static/stationary) data, as well as online update in nonstationary scenarios (adaptive solutions). The performance of this new type of algorithm is analyzed by means of several simulations.