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We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

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 algorithm to find the optimal loci classification subset and then an artificial neural network (ANN) is used to classify patients into dengue fever or severe dengue., 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.