BackgroundNovel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.ResultsWe propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.ConclusionsREGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET.

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The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. 1.IntroductionSince late 1990s, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. Microarray technology has revolutionized the biological research because it allows to study thousand of genes or even whole genomes [1].As molecular biology is rapidly evolving into a quantitative science, it increasingly relies on computational algorithms to make sense of high-throughput data. One of the main goals in Microarray analysis is the reconstruction of gene regulatory processes and a key task is the inference of regulatory interactions among genes from gene expression data [2].Our aimisto infer the relationships between genes from an organism in a particular biological process. This relationships can be modeled in several levels of abstraction, these levels range from the detailed gene regulatory processes (where a chain of intracellular reaction activates a regulatory molecule, transcription factors until a protein is synthesized) to the high models of abstraction named gene association networks. In the reconstruction of gene regulatory processes, building gene association networks has been proven to provide useful insights for such task, the reconstruction of gene regulatory processes. A gene association network can be defined as a graph in which nodes represent genes and edges represent the influence between them. Our goal in this work is the inference of gene association networks from Microarray datasets.There are several statistical methods to infer gene association networks from Microarray data. A microarray dataset is a bidimensional data structure where conditions are experiments or sources and the columns are gene expression values. In our problem the conditions will be the instances and the gene expression values will be the attributes or features. These methods range from relatively straightforward correlation-based methods to more sophisticated methods based on