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The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind

deep learning --- energy demand --- temporal convolutional network --- time series forecasting --- time series --- forecasting --- exponential smoothing --- electricity demand --- residential building --- energy efficiency --- clustering --- decision tree --- time-series forecasting --- evolutionary computation --- neuroevolution --- photovoltaic power plant --- short-term forecasting --- data processing --- data filtration --- k-nearest neighbors --- regression --- autoregression --- n/a

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In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

Research & information: general --- Biology, life sciences --- HIGD2A --- cancer --- DNA methylation --- mRNA expression --- miRNA --- quercetin --- hypoxia --- eQTL --- CRISPR-Cas9 --- single-cell clone --- fine-mapping --- power --- RNA N6-methyladenosine site --- yeast genome --- methylation --- computational biology --- deep learning --- bioinformatics --- hepatocellular carcinoma --- transcriptomics --- proteomics --- bioinformatics analysis --- differentiation --- Gene Ontology --- Reactome Pathways --- gene-set enrichment --- meta-analysis --- transcription factor --- binding sites --- genomics --- chilling stress --- CBF --- DREB --- CAMTA1 --- pathway --- text mining --- infiltration tactics optimization algorithm --- classification --- clustering --- microarray --- ensembles --- machine learning --- infiltration --- computational intelligence --- gene co-expression network --- murine coronavirus --- viral infection --- immune response --- data mining --- systems biology --- obesity --- differential genes expression --- exercise --- high-fat diet --- pathways --- potential therapeutic targets --- DNA N6-methyladenine --- Chou's 5-steps rule --- Convolution Neural Network (CNN) --- Long Short-Term Memory (LSTM) --- machine-learning --- chromatin interactions --- prediction --- genome architecture --- HIGD2A --- cancer --- DNA methylation --- mRNA expression --- miRNA --- quercetin --- hypoxia --- eQTL --- CRISPR-Cas9 --- single-cell clone --- fine-mapping --- power --- RNA N6-methyladenosine site --- yeast genome --- methylation --- computational biology --- deep learning --- bioinformatics --- hepatocellular carcinoma --- transcriptomics --- proteomics --- bioinformatics analysis --- differentiation --- Gene Ontology --- Reactome Pathways --- gene-set enrichment --- meta-analysis --- transcription factor --- binding sites --- genomics --- chilling stress --- CBF --- DREB --- CAMTA1 --- pathway --- text mining --- infiltration tactics optimization algorithm --- classification --- clustering --- microarray --- ensembles --- machine learning --- infiltration --- computational intelligence --- gene co-expression network --- murine coronavirus --- viral infection --- immune response --- data mining --- systems biology --- obesity --- differential genes expression --- exercise --- high-fat diet --- pathways --- potential therapeutic targets --- DNA N6-methyladenine --- Chou's 5-steps rule --- Convolution Neural Network (CNN) --- Long Short-Term Memory (LSTM) --- machine-learning --- chromatin interactions --- prediction --- genome architecture