Thematische Bibliographien / OMIEC

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Auswahl der wissenschaftlichen Literatur zum Thema „OMIEC“

Autor: Grafiati

Veröffentlicht am 29. Juni 2021

Zuletzt aktualisiert am 6. Februar 2022

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Zeitschriftenartikel zum Thema "OMIEC"

Searls, D. B. "Omic Empiricism." Science Signaling 2, no. 68 (2009): eg6-eg6. http://dx.doi.org/10.1126/scisignal.268eg6.

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Fiocchi, Alessandro, and Julie Wang. "-omic sciences." Current Opinion in Allergy and Clinical Immunology 15, no. 3 (2015): 234–36. http://dx.doi.org/10.1097/aci.0000000000000168.

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Rappoport, Nimrod, Roy Safra, and Ron Shamir. "MONET: Multi-omic module discovery by omic selection." PLOS Computational Biology 16, no. 9 (2020): e1008182. http://dx.doi.org/10.1371/journal.pcbi.1008182.

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Morota, Gota. "30 Mutli-omic data integration in quantitative genetics." Journal of Animal Science 97, Supplement_2 (2019): 15. http://dx.doi.org/10.1093/jas/skz122.027.

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Abstract The advent of high-throughput technologies has generated diverse omic data including single-nucleotide polymorphisms, copy-number variation, gene expression, methylation, and metabolites. The next major challenge is how to integrate those multi-omic data for downstream analyses to enhance our biological insights. This emerging approach is known as multi-omic data integration, which is in contrast to studying each omic data type independently. I will discuss challenging issues in developing algorithms and methods for multi-omic data integration. The particular focus will be given to the potential for combining diverse types of FAANG data and the utility of multi-omic data integration in association analysis and phenotypic prediction.

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Major, M. B., and R. T. Moon. ""Omic" Risk Assessment." Science Signaling 2, no. 72 (2009): eg7-eg7. http://dx.doi.org/10.1126/scisignal.272eg7.

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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (2020): 1606. http://dx.doi.org/10.3390/biom10121606.

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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

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Chu, Su, Mengna Huang, Rachel Kelly, et al. "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective." Metabolites 9, no. 6 (2019): 117. http://dx.doi.org/10.3390/metabo9060117.

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It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

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Lin, David, Zsuzsanna Hollander, Anna Meredith, and Bruce M. McManus. "Searching for ‘omic’ biomarkers." Canadian Journal of Cardiology 25 (June 2009): 9A—14A. http://dx.doi.org/10.1016/s0828-282x(09)71048-7.

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Starren, Justin, Marc S. Williams, and Erwin P. Bottinger. "Crossing the Omic Chasm." JAMA 309, no. 12 (2013): 1237. http://dx.doi.org/10.1001/jama.2013.1579.

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Pusta, D. L., A. I. Pastiu, A. Pusta, A. Tabaran, C. M. Raducu, and R. Sobolu. "Relationships between omic sciences." Journal of Biotechnology 305 (November 2019): S84. http://dx.doi.org/10.1016/j.jbiotec.2019.05.291.

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Dissertationen zum Thema "OMIEC"

Heimonen, Johanna. "Synthesis of a polar conjugated polythiophene for 3D-printing of complex coacervates." Thesis, Linköpings universitet, Laboratoriet för organisk elektronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177396.

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The aim of this thesis was to synthesize a functionalized polar conjugated polythiophene that could be (3D-) printed into form-stable structures for bio-interfacing. The material design rationale aimed for a water-processable polymer that had the capability of electronic and ionic conduction, by using a thiophene backbone and oligoethylene side chains. Functionalization of the oligoethylene side chains with carboxylate groups created a polyanion, which allowed for a bio-inspired approach to combine printability and form-stability through formation of complex coacervates. The synthesis of the conjugated monomer and polymer was optimized to provide a more sustainable and material efficient synthesis route. Combined structural analysis with 1H-NMR, FT-IR and UV-vis revealed successful synthesis of the target polymer. Spectro electrochemistry revealed that the polymer was optically and electrochemically active in both the protected and deprotected form. The obtained material is processable from water, and initial tests revealed that crosslinking can be achieved through formation of acid dimers, ionic crosslinks with Ca2+ ions and complex coacervation with a polycation. Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet

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Donate, Puertas Rosa. "Omic approach to atrial fibrillation." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1164.

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La fibrillation atriale (FA) est un problème de santé publique majeur dans le monde entier. Le remodelage électrique, structurel et neuronal est sous-jacent à la myopathie atriale. La pharmacothérapie actuelle est souvent inefficace en raison du manque de connaissance de la pathophysiologie de la FA.Pour comprendre comment se réalise le remodelage atrial, une approche Omique qui explore le transcriptome, l'épigénome (méthylome et microOme) et le génome de patients atteints de FA a été réalisée. Parallèlement, le phénotype de vieux rats spontanément hypertendus (SHRs) a été caractérisé et une étude pharmacologique avec la décitabine (5-Aza-2'-deoxycitidine) a été menée. Les patients atteint de FA présentent un profil transcriptomique et d'expression de miRNA alteré dans l'oreillete gauche (OG), soulignant le rôle important d'un processus de "œmorphogénèse de la structure anatomique". L'expression réduite de Pitx2 était inversement corrélée à la taille de l'OG et ne pouvait pas être expliquée ni par le facteur de transcription ni par la surexpression de Smyd2, une cible de miR-519b. Les SHRs, similairement aux observations chez l'homme, ont développé des arythmies dépendantes de l'âge associées au remodelage atrial et ventriculaire gauche. La FA a été trouvée associée à l'hyperméthylation du promoteur de Pitx2 à la fois chez l'homme et chez les SHRs. L'agent hyperméthylant décitabine a amélioré le profil arhytmique de l'ECG et les activités SOD, et la réduction de la fibrose dans le ventricule gauche des SHRs. En utillisant une approche NGS basée sur un panel personnalisé de 55 gènes candidats à la myopathie atriale dans une cohorte de 94 patients atteints de FA, 11 nouvelles variantes faux-sens potentiellement pathogènes impliqués dans le remodelage structurel ont été identifiés. Des études fonctionnelles de ces variants ont débuté. Trois patients sont également des porteurs de variantes dans les gènes connus de FA. Les résultats actuels suggèrent que 1) la régulation épigénétique peut jouer un rôle dans la pathophysiologie de la FA 2) les agents hypométhylants doivent être considérés comme une nouvelle thérapie de la FA 3) une approche Omique peut aider à découvrir de nouveaux mécanismes sous-jacents à la myopathie atriale Atrial fibrillation (AF) is a major public health care problem worldwide. Electrical, structural, and neural remodeling underlie atrial myopathy. Current pharmacotherapy is often ineffective due to the lack of knowledge of AF pathophysiology. To understand how atrial remodeling occurs, an Omic approach that explore the transcriptome, epigenome (methylome and microOme) and genome of AF patients was performed. In parallel, ageing spontaneously hypertensive rats (SHRs) were phenotypically characterised and a pharmacological study with decitabine (5-Aza-2’-deoxycitidine) was conducted. AF patients presented an altered transcriptomic and microRNA expression profile in the left atria (LA), emphasizing the important role of an "anatomical structure morphogenesis" process. The Pitx2 reduced expression was inversely correlated with LA size, and could not be explained by transcriptor factor. Smyd2 is a target of miR-519b-3p. SHRs, similar to what is observed in humans, developed age-dependent arrhythmias associated with left atrial and ventricular remodeling. AF was found to be associated with Pitx2 promoter hypermethylation both in humans and in SHRs. The hypomethylating agent decitabine improved ECG arrhythmic profiles and superoxide dismutase activities, and reduced fibrosis in the left ventricle of SHRs. Using a next-generation sequencing approach based on a custom panel of 55 atrial myopathy candidate genes in a cohort of 94 AF patients, 11 novel potentially pathogenic missense variants involved in structural remodeling were identified. Functional studies of these variants have started. Three patients were also carriers of variants in known AF-causing genes. The present results suggest that 1) epigenetic regulation may play a role in the pathophysiology of AF 2) hypomethylating agents have to be considered as a new AF therapy 3) an Omic approach may help to uncover new mechanisms underlying atrial myopathy

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Bilbrey, Emma A. "Seeding Multi-omic Improvement of Apple." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594907111820227.

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Guan, Xiaowei. "Bioinformatics Approaches to Heterogeneous Omic Data Integration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1340302883.

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Rossouw, Debra. "Comparative 'omic' profiling of industrial wine yeast strains." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/1454.

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Thesis (PhD(Agric) Viticulture and Oenology. Wine Biotechnology))--University of Stellenbosch, 2009. The main goal of this project was to elucidate the underlying genetic factors responsible for the different fermentation phenotypes and physiological adaptations of industrial wine yeast strains. To address this problem an ‘omic’ approach was pursued: Five industrial wine yeast strains, namely VIN13, EC1118, BM45, 285 and DV10, were subjected to transcriptional, proteomic and exometabolomic profiling during alcoholic fermentation in simulated wine-making conditions. The aim was to evaluate and integrate the various layers of data in order to obtain a clearer picture of the genetic regulation and metabolism of wine yeast strains under anaerobic fermentative conditions. The five strains were also characterized in terms of their adhesion/flocculation phenotypes, tolerance to various stresses and survival under conditions of nutrient starvation. Transcriptional profiles for the entire yeast genome were obtained for three crucial stages during fermentation, namely the exponential growth phase (day 2), early stationary phase (day 5) and late stationary phase (day 14). Analysis of changes in gene expression profiles during the course of fermentation provided valuable insights into the genetic changes that occur as the yeast adapt to changing conditions during fermentation. Comparison of differentially expressed transcripts between strains also enabled the identification of genetic factors responsible for differences in the metabolism of these strains, and paved the way for genetic engineering of strains with directed modifications in key areas. In particular, the integration of exo-metabolite profiles and gene expression data for the strains enabled the construction of statistical models with a strong predictive capability which was validated experimentally. Proteomic analysis enabled correlations to be made between relative transcript abundance and protein levels for approximately 450 gene and protein pairs per analysis. The alignment of transcriptome and proteome data was very accurate for interstrain comparisons. For intrastrain comparisons, there was almost no correlation between trends in protein and transcript levels, except in certain functional categories such as metabolism. The data also provide interesting insights into molecular evolutionary mechanisms that underlie the phenotypic diversity of wine yeast strains. Overall, the systems biology approach to the study of yeast metabolism during alcoholic fermentation opened up new avenues for hypothesis-driven research and targeted engineering strategies for the genetic enhancement/ modification of wine yeast for commercial applications.

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Xiao, Hui. "Network-based approaches for multi-omic data integration." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289716.

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The advent of advanced high-throughput biological technologies provides opportunities to measure the whole genome at different molecular levels in biological systems, which produces different types of omic data such as genome, epigenome, transcriptome, translatome, proteome, metabolome and interactome. Biological systems are highly dynamic and complex mechanisms which involve not only the within-level functionality but also the between-level regulation. In order to uncover the complexity of biological systems, it is desirable to integrate multi-omic data to transform the multiple level data into biological knowledge about the underlying mechanisms. Due to the heterogeneity and high-dimension of multi-omic data, it is necessary to develop effective and efficient methods for multi-omic data integration. This thesis aims to develop efficient approaches for multi-omic data integration using machine learning methods and network theory. We assume that a biological system can be represented by a network with nodes denoting molecules and edges indicating functional links between molecules, in which multi-omic data can be integrated as attributes of nodes and edges. We propose four network-based approaches for multi-omic data integration using machine learning methods. Firstly, we propose an approach for gene module detection by integrating multi-condition transcriptome data and interactome data using network overlapping module detection method. We apply the approach to study the transcriptome data of human pre-implantation embryos across multiple development stages, and identify several stage-specific dynamic functional modules and genes which provide interesting biological insights. We evaluate the reproducibility of the modules by comparing with some other widely used methods and show that the intra-module genes are significantly overlapped between the different methods. Secondly, we propose an approach for gene module detection by integrating transcriptome, translatome, and interactome data using multilayer network. We apply the approach to study the ribosome profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose an approach for feature selection by integrating transcriptome and interactome data using network-constrained regression. We develop a more efficient network-constrained regression method eGBL. We evaluate its performance in term of variable selection and prediction, and show that eGBL outperforms the other related regression methods. With application on the transcriptome data of human blastocysts, we select several interested genes associated with time-lapse parameters. Finally, we propose an approach for classification by integrating epigenome and transcriptome data using neural networks. We introduce a superlayer neural network (SNN) model which learns DNA methylation and gene expression data parallelly in superlayers but with cross-connections allowing crosstalks between them. We evaluate its performance on human breast cancer classification. The SNN provides superior performances and outperforms several other common machine learning methods. The approaches proposed in this thesis offer effective and efficient solutions for integration of heterogeneous high-dimensional datasets, which can be easily applied to other datasets presenting the similar structures. They are therefore applicable to many fields including but not limited to Bioinformatics and Computer Science.

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Martínez, Enguita David. "Identification of personalized multi-omic disease modules in asthma." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15987.

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Asthma is a respiratory syndrome associated with airflow limitation, bronchial hyperresponsiveness and inflammation of the airways in the lungs. Despite the ongoing research efforts, the outstanding heterogeneity displayed by the multiple forms in which this condition presents often hampers the attempts to determine and classify the phenotypic and endotypic biological structures at play, even when considering a limited assembly of asthmatic subjects. To increase our understanding of the molecular mechanisms and functional pathways that govern asthma from a systems medicine perspective, a computational workflow focused on the identification of personalized transcriptomic modules from the U-BIOPRED study cohorts, by the use of the novel MODifieR integrated R package, was designed and applied. A feature selection of candidate asthma biomarkers was implemented, accompanied by the detection of differentially expressed genes across sample categories, the production of patient-specific gene modules and the subsequent construction of a set of core disease modules of asthma, which were validated with genomic data and analyzed for pathway and disease enrichment. The results indicate that the approach utilized is able to reveal the presence of components and signaling routes known to be crucially involved in asthma pathogenesis, while simultaneously uncovering candidate genes closely linked to the latter. The present project establishes a valuable pipeline for the module-driven study of asthma and other related conditions, which can provide new potential targets for therapeutic intervention and contribute to the development of individualized treatment strategies.

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Elhezzani, Najla Saad R. "New statistical methodologies for improved analysis of genomic and omic data." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/new-statistical-methodologies-for-improved-analysis-of-genomic-and-omic-data(eb8d95f4-e926-4c54-984f-94d86306525a).html.

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We develop statistical tools for analyzing different types of phenotypic data in genome-wide settings. When the phenotype of interest is a binary case-control status, most genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is very rare; otherwise, a loss in the statistical power to detect disease-associated variants is expected. To address this, we propose a joint analysis of the three types of samples; cases, bases and controls. This is done by modeling the bases as a mixture of multinomial logistic functions of cases and controls, according to disease prevalence. In a typical GWAS, where thousands of single-nucleotide polymorphisms (SNPs) are available for testing, score-based test statistics are ideal in this case. Other tests of associations such as Wald’s and likelihood ratio tests are known to be asymptotically equivalent to the score test, however their performance under small sample sizes can vary significantly. In order to allow the test comparison to be performed under the proposed case-base-control (CBC) design, we provide an estimation procedure using the maximum likelihood (ML) method along with the expectation-maximization (EM) algorithm. Simulations show that combining the three samples can increase the power to detect disease-associated variants, though a very large base sample set can compensate for lack of controls. In the second part of the thesis, we consider a joint analysis of both genome-wide SNPs as well as multiple phenotypes, with a focus on the challenges they present in the estimation of SNP heritability. The current standard for performing this task is fit-ting a variance component model, despite its tendency to produce boundary estimates when small sample sizes are used. We propose a Bayesian covariance component model (BCCM) that takes into account genetic correlation among phenotypes and genetic correlation among individuals. The use of Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP heritability estimates over univariate and ML-based methods, thus explaining why recent progress in eQTL identification has been limited. I published this work as an article in the European Journal of Human genetics. In the third part of the thesis, we study the prospects of using the proposed BCCM for phenotype prediction. Results from real data show consistency in accuracy between ML based methods and the proposed Bayesian method, when effect sizes are estimated using their posterior mode. It is also noted that an initial imputation step relatively increases the predictive accuracy.

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Zuo, Yiming. "Differential Network Analysis based on Omic Data for Cancer Biomarker Discovery." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78217.

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Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. Ph. D.

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Tsai, Tsung-Heng. "Bayesian Alignment Model for Analysis of LC-MS-based Omic Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64151.

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Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and provides estimates of the retention time variability along with uncertainty measures, enabling a natural framework to integrate information of various sources. From methodology development to practical application, we investigate the alignment problem through three research topics: 1) development of single-profile Bayesian alignment model, 2) development of multi-profile Bayesian alignment model, and 3) application to biomarker discovery research. Chapter 2 introduces the profile-based Bayesian alignment using a single chromatogram, e.g., base peak chromatogram from each LC-MS run. The single-profile alignment model improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS). Chapter 3 extends the model to integrate complementary information that better captures the variability in chromatographic separation. We use Gaussian process regression on the internal standards to derive a prior distribution for the mapping functions. In addition, a clustering approach is proposed to identify multiple representative chromatograms for each LC-MS run. With the Gaussian process prior, these chromatograms are simultaneously considered in the profile-based alignment, which greatly improves the model estimation and facilitates the subsequent peak matching process. Chapter 4 demonstrates the applicability of the proposed Bayesian alignment model to biomarker discovery research. We integrate the proposed Bayesian alignment model into a rigorous preprocessing pipeline for LC-MS data analysis. Through the developed analysis pipeline, candidate biomarkers for hepatocellular carcinoma (HCC) are identified and confirmed on a complementary platform. Ph. D.

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Bücher zum Thema "OMIEC"

Gupta, Sanjeev, Nagasamy Nadarajan, and Debjyoti Sen Gupta, eds. Legumes in the Omic Era. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-8370-0.

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Daniels, Ronald J. Econ omic analysis of law. Faculty of Law, University of Toronto, 1991.

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Haapanen, Atso. Asevelisurmat: Kenttäoikeuksissa vuosina 1939-1944 omien sotilaiden surmista tuomitut. Minerva, 2013.

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Kop, Hans van der. Omie en Eddie: Een Indisch familieleven, 1872-1955. Eisma, 1996.

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Cytrynowicz, Roney, and Monica Musatti Cytrynowicz. OMEC UMC: Universidade de Mogi das Cruzes : 1962-2002. s.n., 2002.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Roberts, Simon David. Econ omic and monetary union and the peripheral regions of the European Union. The Author], 1996.

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Buchteile zum Thema "OMIEC"

Saitou, Naruya. "Omic Data Collection." In Introduction to Evolutionary Genomics. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5304-7_12.

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Feng, Weiyue. "“Omic” Techniques for Nanosafety." In Toxicology of Nanomaterials. Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527689125.ch12.

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Saitou, Naruya. "Omic Worlds and Their Databases." In Introduction to Evolutionary Genomics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92642-1_14.

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Avramouli, Antigoni, and Panayiotis M. Vlamos. "Integrating Omic Technologies in Alzheimer’s Disease." In Advances in Experimental Medicine and Biology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57379-3_16.

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Gupta, Sanjeev, Nagasamy Nadarajan, and Debjyoti Sen Gupta. "Legumes in Omic Era: Retrospects and Prospects." In Legumes in the Omic Era. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8370-0_1.

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Thavarajah, Dil, Pushparajah Thavarajah, and Debjyoti Sen Gupta. "Pulses Biofortification in Genomic Era: Multidisciplinary Opportunities and Challenges." In Legumes in the Omic Era. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8370-0_10.

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Pratap, Aditya, Rakhi Tomar, Neha Rajan, et al. "Towards Enriching Genomic Resources in Legumes." In Legumes in the Omic Era. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8370-0_11.

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Singh, Vinay Kumar, A. K. Singh, Arvind M. Kayastha, and B. D. Singh. "Bioinformatics for Legume Genomics Research." In Legumes in the Omic Era. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8370-0_12.

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Saha, Gopesh C., and Fred J. Muehlbauer. "Genetics and Genomics of Resistance to Rust and Stemphylium Blight in Lentil." In Legumes in the Omic Era. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8370-0_13.

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Kumar, Jitendra, Ekta Srivastava, Mritunjay Singh, and Aditya Pratap. "Genomics in Studying the Legume Genome Evolution." In Legumes in the Omic Era. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8370-0_14.

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Konferenzberichte zum Thema "OMIEC"

Zhukov, V. A., A. M. Afonin, G. A. Akhtemova, et al. "Study of the garden pea (Pisum sativum L.) symbioses in post-genomic era." In 2nd International Scientific Conference "Plants and Microbes: the Future of Biotechnology". PLAMIC2020 Organizing committee, 2020. http://dx.doi.org/10.28983/plamic2020.289.

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Mutualistic symbioses formed by garden pea have been studied with use of ‘omic’ technologies in order to gain a new understanding of molecular mechanisms of beneficial effect that microsymbionts have on seed yield and quality.  Keywords: garden pea, transcriptomics, nitrogen fixation, arbuscular mycorrhiza, PGPB

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Bardozzo, Francesco, Pietro Lio, and Roberto Tagliaferri. "Multi omic oscillations in bacterial pathways." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280853.

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Keir, Holly Rachael, Amelia Shoemark, Megan Crichton, et al. "Endotyping bronchiectasis through multi-omic profiling." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.4101.

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Otero-Núñez, Pablo, Christopher Rhodes, John Wharton, et al. "Multi-omic profiling in pulmonary arterial hypertension." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.4458.

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Resson, Habtom W. "Multi-omic approaches for liver cancer biomarker discovery." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822481.

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Zuo, Yiming, Guoqiang Yu, Chi Zhang, and Habtom W. Ressom. "A new approach for multi-omic data integration." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999157.

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Ressom, Habtom W., Cristina Di Poto, Alessia Ferrarini, et al. "Multi-omic approaches for characterization of hepatocellular carcinoma." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591467.

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Fan, Ziling, Yuan Zhou, and Habtom W. Ressom. "MOTA: Multi-omic integrative analysis for biomarker discovery." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857049.

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Louis, Joe. ""Omic" approaches to decipher plant defense mechanisms against insect pests." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.93755.

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Becker, Timothy James, and Dong-Guk Shin. "HFM: Hierarchical Feature Moment Extraction for Multi-Omic Data Visualization." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983015.

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Berichte der Organisationen zum Thema "OMIEC"

Banfield, Jill. Multi-‘omic’ analyses of the dynamics, mechanisms, and pathways for carbon turnover in grassland soil under two climate regimes. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1504276.

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Pokrzywinski, Kaytee, Kaitlin Volk, Taylor Rycroft, Susie Wood, Tim Davis, and Jim Lazorchak. Aligning research and monitoring priorities for benthic cyanobacteria and cyanotoxins : a workshop summary. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41680.

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In 2018, the US Army Engineer Research and Development Center partnered with the US Army Corps of Engineers–Buffalo District, the US Environmental Protection Agency, Bowling Green State University, and the Cawthron Institute to host a workshop focused on benthic and sediment-associated cyanobacteria and cyanotoxins, particularly in the context of harmful algal blooms (HAB). Technical sessions on the ecology of benthic cyanobacteria in lakes and rivers; monitoring of cyanobacteria and cyanotoxins; detection of benthic and sediment-bound cyanotoxins; and the fate, transport, and health risks of cyanobacteria and their associated toxins were presented. Research summaries included the buoyancy and dispersal of benthic freshwater cyanobacteria mats, the fate and quantification of cyanotoxins in lake sediments, and spatial and temporal variation of toxins in streams. In addition, summaries of remote sensing methods, omic techniques, and field sampling techniques were presented. Critical research gaps identified from this workshop include (1) ecology of benthic cyanobacteria, (2) identity, fate, transport, and risk of cyanotoxins produced by benthic cyanobacteria, (3) standardized sampling and analysis protocols, and (4) increased technical cooperation between government, academia, industry, nonprofit organizations, and other stakeholders. Conclusions from this workshop can inform monitoring and management efforts for benthic cyanobacteria and their associated toxins.

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