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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Medical microbiology & virology --- Microbiology (non-medical) --- microbiome --- High dimensional data --- bioinformatics --- Biostatistics --- data visualization --- microbiome --- High dimensional data --- bioinformatics --- Biostatistics --- data visualization

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Medical microbiology & virology --- Microbiology (non-medical) --- microbiome --- High dimensional data --- bioinformatics --- Biostatistics --- data visualization

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The Research Topic is designed to feature the latest innovative and leading-edge research, reviews and opinions on the study of complex and dynamic processes related to the mammalian immune system and cancer. All papers will be meticulously selected to present our readers the multidisciplinary approach to tackle the existing challenges faced in these important fields. From high throughput experimental methodologies to computational and theoretical approaches, the articles are intended to introduce physicists, chemists, computer scientists, biologists and immunologists the idea of systems biology approach to the understanding of mammalian immune system and cancer processes. Attention will also be given to works that develop more effective approaches to the treatment of proinflammatory disease and cancer. The strong interdisciplinary focus will discuss biological systems at the level from a few molecules to the entire organism. Specific focus domain includes: Innate and adaptive immunity, Cancer and cancer stem cell, Genomic, proteomic and metabolic analysis, Imaging, Biophysics of immune and cancer response, Computational modeling, Non-linear analysis, Statistical analysis, Translational and disease models.

Immune system --- Cancer --- Research. --- Immunological aspects. --- Nonparametric --- High dimensional data --- Computational Biology --- plasticity --- Systems Biology --- immunology --- Cancer --- statistics

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The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.

Humanities --- Social interaction --- high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.

Humanities --- Social interaction --- high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining --- high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining