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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.

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 --- n/a --- lipid-environment interaction

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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 --- n/a --- lipid-environment interaction

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The Mediterranean diet is well-known worldwide and recognized as a nutrition reference model by the World Health Organization. Virgin olive oil, prepared from healthy and intact fruits of the olive tree only by mechanical means, is a basic ingredient and a real pillar of this diet. Its positive role in health has now been a topic of universal concern. The virtues of natural olive oil, and especially of extra virgin olive oil, are related to the quality of the fruits, the employment of advanced technologies, and the availability of sophisticated analytical techniques that are used to control the origin of the fruits and guarantee the grade of the final product. To enrich recent multidisciplinary scientific information concerning this healthy lipid source, a new special issue of Foods has been published.

Research & information: general --- Biology, life sciences --- Food & society --- 1H NMR spectroscopy --- EVOOs geographical origin --- extra virgin olive oil --- multivariate statistical analysis --- historical climate data --- olive oil --- biophenols --- Mediterranean diet --- pharma-nutrition --- cardiovascular disease --- authentication --- bioactive --- by-product --- glycolipid --- lipidomics --- mass spectrometry --- phospholipid --- traceability --- extra-virgin olive oil --- EVOO --- chlorophylls --- carotenoids --- pigments --- colour --- quality --- spectroscopy --- ultraviolet-visible light --- light absorption --- successful aging --- dietary fats --- authenticity --- cultivars --- triacylglycerols --- volatiles --- chemometrics --- extraction process --- innovative approaches --- ultrasound --- technological level of readiness --- varietal authentication --- Nuclear Magnetic Resonance spectroscopy --- polar lipids --- bio-phenols --- pharmaceutical molecular mechanisms of action --- primary public health prevention strategies --- healthy aging and longevity

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Metabolomics has been a useful method for various study fields. However, its application in animal science does not seem to be sufficient. Metabolomics will be useful for various studies in animal science: Animal genetics and breeding, animal physiology, animal nutrition, animal products (milk, meat, eggs, and their by-products) and their processing, livestock environment, animal biotechnology, animal behavior, and animal welfare. More application examples and protocols for animal science will promote more motivation to use metabolomics effectively in the study field. Therefore, in this Special Issue, we introduced some research and review articles for “Metabolomic Applications in Anmal Science”. The main methods used were mass spectrometry or nuclear magnetic resonance spectroscopy. Not only a non-targeted, but also a targeted, analysis of metabolites is shown. The topics include dietary and pharmacological interventions and protocols for metabolomic experiments.

albumen --- breed --- chicken --- feed --- metabolome --- yolk --- arachidonic acid --- omega-3 fatty acids --- lipidomics --- mass spectrometry --- dietary fat --- fatty acid metabolism --- pork --- meat --- skeletal muscle --- fiber type --- cooking --- beef --- Wagyu --- Holstein --- captive giraffes --- urine --- metabolomics --- 1H-NMR --- NMR --- metabotype --- transition --- ketosis --- cattle --- chemometrics --- spectral correction --- authentication --- biomarker --- feeding --- meat quality traits --- metabolite --- postmortem aging --- processing --- chickens --- heat stress --- lipid peroxidation --- orotic acid --- feed efficiency --- biomarkers --- SNPs --- GWAS --- RFI --- pigs --- pathways --- metabolic profile --- transition period --- livestock --- methyl donor --- one-carbon metabolism --- negative energy balance --- pasture legumes --- phytoestrogens --- flavonoids --- coumestans --- polyphenols --- proanthocyanidins --- metabolic profiling --- biosynthesis --- linear model --- transcriptomics --- horse --- metabolomic --- metabolism --- exercise --- saliva --- anabolic practices --- testosterone --- plasma --- CE-TOFMS --- intramuscular fat --- meat quality --- porcine

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Genomics technologies revolutionised biomedicine research, but the genome alone is not sufficient to capture biological complexity. Postgenomic methods, typically based on mass spectrometry, comprise the analysis of metabolites, lipids, and proteins and are an essential complement to genomics and transcriptomics. Multidimensional omics is becoming established to provide accurate and comprehensive state descriptions. This book covers the latest methodological developments for, and applications of integrative multi-omics in biomedical research.

target identification --- target validation --- label-free method for drugs --- anti-angiogenesis --- mechanism of action --- receptor tyrosine kinases --- curcumin --- natural products --- lipid --- lipidomics --- cardiac metaplasia --- Barrett’s esophagus --- esophageal adenocarcinoma --- microbiota --- DNA sensing --- IFI16 --- cGAS --- innate immunity --- protein interactions --- virus–host interactions --- post-translational modifications --- mass spectrometry --- proteomics --- transcriptomics --- multi-omics --- multi-omics analysis --- study design --- bioinformatics --- machine learning --- analysis flow --- metabolomics --- planned myocardial infarction (PMI) --- myocardial infarction (MI) --- exercise --- heart --- cheminformatics --- batch variations --- eicosanoids --- fetal calf serum --- peroxisomes --- host-pathogen interactions --- secretome --- macrophages --- acute myeloid leukemia --- HL-60 cell line --- ATRA --- induced differentiation --- transcriptome --- proteome --- transcription factors --- key molecules --- regulatory pathway modelling --- SRM --- endometriosis --- inflammation --- n/a --- Barrett's esophagus --- virus-host interactions