Abstract
Cancer genome projects are characterizing the genome, epigenome and transcriptome of a large number of samples using the latest high-throughput sequencing assays. The generated data sets pose several challenges for traditional statistical and machine learning methods. In this work we are interested in the task of deriving the most informative genes from a cancer gene expression data set. For that goal we built denoising autoencoders (DAE) and stacked denoising autoencoders and we studied the influence of the input nodes on the final representation of the DAE. We have also compared these deep learning approaches with other existing approaches. Our study is divided into two main tasks. First, we built and compared the performance of several feature extraction methods as well as data sampling methods using classifiers that were able to distinguish the samples of thyroid cancer patients from samples of healthy persons. In the second task, we have investigated the possibility of building comprehensible descriptions of gene expression data by using Denoising Autoencoders and Stacked Denoising Autoencoders as feature extraction methods. After extracting information related to the description built by the network, namely the connection weights, we devised post-processing techniques to extract comprehensible and biologically meaningful descriptions out of the constructed models. We have been able to build high accuracy models to discriminate thyroid cancer from healthy patients but the extraction of comprehensible models is still very limited.

Abstract

Abstract
Recent advances in sequencing allow the study of all identified human genes (22,000 protein encoding genes), which have differential expression between tissues. However, current knowledge on gene interactions lags behind, especially when one of the elements encodes a mitochondrial protein (1500). Mitochondrial proteins are encoded either by mitochondrial DNA (mtDNA; 13 proteins) or by nuclear DNA (nDNA; the remaining), which implies a coordinated communication between the two genomes. Since mitochondria coordinate several life-critical cellular activities, namely energy production and cell death, deregulation of this communication is implicated in many complex diseases such as neurodegenerative diseases, cancer and diabetes. Thus, this work aimed to identify high co-expression groups between mitochondrial genes-all genes, and associated protein networks in several human tissues (Genotype-Tissue Expression database). We developed a pipeline and a web tree viewer that is available at GitHub (https://github.com/Pereira-lab/CoExpression). Biologically, we confirmed the existence of highly correlated pairs of mitochondrial-all protein encoding genes, which act in pathways of functional importance such as energy production and metabolite synthesis, especially in brain tissues. The strongest correlation between mtDNA genes are with genes encoded by this genome, showing that correlation among genes encoded by the same genome is more efficient.

Abstract

Abstract
VASO is a Portuguese participatory maize breeding project (1984), where several maize landraces such as Pigarro have been selected both by a farmer's (phenotypic recurrent selection) and a breeder's approach (S2 lines recurrent selection). The objectives of this study were to determine the phenotypic and genotypic responses to participatory selection using these two different approaches, to clarify to which extent both selection methods preserve genetic diversity, and conclude what is the preferred method to apply in sustainable farming systems. The results, obtained via ANOVA, regression analyses and molecular markers, indicate that for both selection methods, genetic diversity was not significantly reduced, even with the most intensive breeder's selection. Although there were some common outputs, such as the determinated versus indeterminated ears, cob and ear weight ratio per ear and rachis 2, specific phenotypic traits evolved in opposite directions between the two selection approaches. Yield increase was only detected during farmer selection, indicating its interest on PPB. Candidate genes were identified for a few of the traits under selection as potential functional markers in participatory plant breeding.

Abstract
The environmental concerns are having a significant impact on the operation of power systems. The traditional Unit Commitment problem (UCP), which minimizes the total production costs is inadequate when environmental emissions need to be considered in the operation of power plants. This paper proposes a metaheuristic approach combined with a non-dominated sorting procedure to find solutions for the multi-objective UCP. The metaheuristic proposed, a Biased Random Key Genetic Algorithm, is a variant of the random-key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. (C) 2017 The Authors. Published by Elsevier Ltd.