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An important, open research topic today is to understand the relevance that dark matter halo substructure may have for dark matter searches. In the standard cosmological model, halo substructure or subhalos are predicted to be largely abundant inside larger halos, for example, galaxies such as ours, and are thought to form first and later merge to form larger structures. Dwarf satellite galaxies—the most massive exponents of halo substructure in our own galaxy—are already known to be excellent targets for dark matter searches, and indeed, they are constantly scrutinized by current gamma-ray experiments in the search for dark matter signals. Lighter subhalos not massive enough to have a visible counterpart of stars and gas may be good targets as well, given their typical abundances and distances. In addition, the clumpy distribution of subhalos residing in larger halos may boost the dark matter signals considerably. In an era in which gamma-ray experiments possess, for the first time, the exciting potential to put to test the preferred dark matter particle theories, a profound knowledge of dark matter astrophysical targets and scenarios is mandatory should we aim for accurate predictions of dark matter-induced fluxes for investing significant telescope observing time on selected targets and for deriving robust conclusions from our dark matter search efforts. In this regard, a precise characterization of the statistical and structural properties of subhalos becomes critical. In this Special Issue, we aim to summarize where we stand today on our knowledge of the different aspects of the dark matter halo substructure; to identify what are the remaining big questions, and how we could address these; and, by doing so, to find new avenues for research.

gamma rays --- indirect searches. --- semi-analytic modeling --- cosmological model --- indirect dark matter searches --- particle dark matter --- indirect detection --- gamma-rays and neutrinos --- galactic subhalos --- indirect searches --- statistical data analysis --- subhalo boost --- dark matter halos --- halo substructure --- structure formation --- dark matter annihilation --- dark matter searches --- dwarf spheroidal satellite galaxies --- galactic sub-halos --- subhalos --- dwarf spheroidal galaxies --- gamma-rays --- cosmological N-body simulations --- dark matter

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In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Research & information: general --- Mathematics & science --- multiple cancer types --- integrative analysis --- omics data --- prognosis modeling --- classification --- gene set enrichment analysis --- boosting --- kernel method --- Bayes factor --- Bayesian mixed-effect model --- CpG sites --- DNA methylation --- Ordinal responses --- GEE --- lipid-environment interaction --- longitudinal lipidomics study --- penalized variable selection --- convolutional neural networks --- deep learning --- feed-forward neural networks --- machine learning --- gene regulatory network --- nonparanormal graphical model --- network substructure --- false discovery rate control --- gaussian finite mixture model --- clustering analysis --- uncertainty --- expectation-maximization algorithm --- classification boundary --- gene expression --- RNA-seq