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Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. This Special Issue of Mathematics (ISSN 2227-7390) includes diverse modern data analysis methods such as skew-reflected-Gompertz information quantifiers with application to sea surface temperature records, the performance of variable selection and classification via a rank-based classifier, two-stage classification with SIS using a new filter ranking method in high throughput data, an estimation of sensitive attribute applying geometric distribution under probability proportional to size sampling, combination of ensembles of regularized regression models with resampling-based lasso feature selection in high dimensional data, robust linear trend test for low-coverage next-generation sequence data controlling for covariates, and comparing groups of decision-making units in efficiency based on semiparametric regression.

Kullback–Leibler divergence --- geometric distribution --- accuracy --- AUROC --- allele read counts --- mixture model --- low-coverage --- entropy --- gene-expression data --- SCAD --- data envelopment analysis --- LASSO --- high-throughput --- sandwich variance estimator --- adaptive lasso --- semiparametric regression --- ?1 lasso --- Laplacian matrix --- elastic net --- feature selection --- sea surface temperature --- gene expression data --- Skew-Reflected-Gompertz distribution --- lasso --- next-generation sequencing --- BH-FDR --- stochastic frontier model --- ?2 ridge --- geometric mean --- resampling --- Gompertz distribution --- adapative lasso --- group efficiency comparison --- sensitive attribute --- MCP --- probability proportional to size (PPS) sampling --- randomization device --- SIS --- Yennum et al.’s model --- ensembles

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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.

Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection