GA-Based Data Mining Applied to Genetic Data for the Diagnosis of Complex Diseases

Nature provides inspiration and guidance in the creation of techniques, applications and new technologies in the fields of Artificial Intelligence and Soft Computing. Soft Computing Methods for Practical Environment Solutions: Techniques and Studies presents various practical applications of Soft Computing techniques in real-world situations and problems, aiming to show the enormous potential of such techniques in solving all kinds of problems, and thus, providing the latest advances in these techniques in an extensive state-of-the-art and a vast theoretical study. Ideal for students studying AI and researchers familiarizing themselves with such techniques, so to offer recent and novel applications, helping expand and explore new areas of research. Nature provides inspiration and guidance in the creation of techniques, applications and new technologies in the fields of Artificial Intelligence and Soft Computing. Soft Computing Methods for Practical Environment Solutions: Techniques and Studies presents various practical applications of Soft Computing techniques in real-world situations and problems, aiming to show the enormous potential of such techniques in solving all kinds of problems, and thus, providing the latest advances in these techniques in an extensive state-of-the-art and a vast theoretical study. Ideal for students studying AI and researchers familiarizing themselves with such techniques, so to offer recent and novel applications, helping expand and explore new areas of research

Approach for solving multimodal problems using genetic algorithms with grouped into species optimized with predator-prey

Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness

Net-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Prediction

Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs represents a difficult task due to the large number of edges and the complex connectivity patterns. Fortunately, we can use another special type of networks to achieve this goal-Artificial Neural Networks (ANNs). Thus, ANNs could use node descriptors such as Shannon Entropies (Sh) to predict node connectivity for large datasets including complex systems such as BCN. However, the training of a high number of ANNs for BCNs is a time-consuming task. In this work, we propose the use of a method to automatically determine which ANN topology is more efficient for the BCN prediction. Since a network (ANN) is used to predict the connectivity in another network (BCN), this method was entitled Net-Net AutoML. The algorithm uses Sh descriptors for pairs of nodes in BCNs and for ANN predictors of BCNs. Therefore, it is able to predict the efficiency of new ANN topologies to predict BCNs. The current study used a set of 500,470 examples from 10 different ANNs to predict node connectivity in BCNs and 20 features. After testing five Machine Learning classifiers, the best classification model to predict the ability of an ANN to evaluate node interactions in BCNs was provided by Random Forest (mean test AUROC of 0.9991 +/- 0.0001, 10-fold cross-validation). Net-Net AutoML algorithms based on entropy descriptors may become a useful tool in the design of automatic expert systems to select ANN topologies for complex biological systems. The scripts and dataset for this project are available in an open GitHub repository

Efficacy and Safety of Inhaled Ethanol in Early-Stage SARS-CoV-2 Infection in Older Adults: A Phase II Randomized Clinical Trial

Background: Inhaled ethanol in the early stages of SARS-CoV-2 infection may reduce the viral load, decreasing progression and improving prognosis. The ALCOVID-19 trial was designed to study the efficacy and safety of inhaled ethanol in older adults at initial phases of infection. Methods: Randomized, triple-blind, placebo-controlled phase II clinical trial. Experimental group (n = 38) inhaled 65° ethanol through an oxygen flow, while in the control group (n = 37), water for injection was used. General endpoint was to evaluate disease progression according to the modified World Health Organization (WHO) Clinical Progression Scale. Specific effectiveness endpoints were body temperature, oxygen saturation, viral load assessed by cycle threshold (Ct) on real-time polymerase chain reaction (RT-PCR), analytical biomarkers and use of antibiotics or corticosteroids. Specific safety outcomes were the absence of ethanol in plasma, electrographic, analytical, or respiratory alterations. Results: In the intention-to-treat population, no differences were found regarding disease progression. Mean Ct values increased over time in both groups, being numerically higher in the ethanol group, reaching a value above 33 only in the ethanol group on day 14, a value above which patients are considered non-infective. No differences were found in the other specific effectiveness endpoints. Inhaled ethanol was proven to be safe as no plasma ethanol was detected, and there were no electrocardiographic, analytical, or respiratory alterations. Conclusions: The efficacy of inhaled ethanol in terms of the progression of SARS-CoV-2 infection was not demonstrated in the present trial. However, it is positioned as a safe treatment for elderly patients with early-stage COVID-19