Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate. Objective: We present a machine learning approach for the prediction of dengue fever severity based solely on human genome data. Methods: One hundred and two Brazilian dengue patients and controls were genotyped for 322 innate immunity Single Nucleotide Polymorphisms (SNPs). Our model uses a Support Vector Machine (SVM) algorithm to find the optimal loci classification subset, and then an Artificial Neural Network (ANN) is used to classify patients into dengue fever (DF) or severe dengue (SD). Results: The ANN trained on 13 key immune SNPs selected under dominant or recessive models produced median values of accuracy greater than 86%, and sensitivity and specificity over 98% and 51%, respectively. Conclusion: The proposed classification method, using only genome markers, can be used to identify individuals at high risk for developing the severe dengue phenotype even in uninfected conditions. Significance: Our results suggest that the genetic context is a key element in phenotype definition in dengue. The methodology proposed here is extendable to other Mendelian-based and genetically influenced diseases.

A customized version of Fish School Search (FSS) algorithm and the innovative volitive operator of FSS (which is incorporated into the regular particle swarm optimization (PSO) algorithm) are applied to the finite element model (FEM) updating problem

In this paper, we propose a new method for surface reconstruction based on growing self-organizing maps (SOMs), called growing self-reconstruction maps (GSRMs). GSRM is an extension of growing neural gas (GNG) that includes the concept of triangular faces in the learning algorithm and additional conditions in order to include and remove connections, so that it can produce a triangular two-manifold mesh representation of a target object given an unstructured point cloud of its surface. The main modifications concern competitive Hebbian learning (CHL), the vertex insertion operation, and the edge removal mechanism. The method proposed is able to learn the geometry and topology of the surface represented in the point cloud and to generate meshes with different resolutions. Experimental results show that the proposed method can produce models that approximate the shape of an object, including its concave regions, boundaries, and holes, if any.