Journal articles: 'OMIEC'

1

Searls, D. B. "Omic Empiricism." Science Signaling 2, no. 68 (April 21, 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 (June 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 (September 15, 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 (July 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 (May 19, 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 (November 27, 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, Elisa Benedetti, Jalal Siddiqui, Oana Zeleznik, Alexandre Pereira, et al. "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective." Metabolites 9, no. 6 (June 18, 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 (March 27, 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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Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, Taylor Donnelly, Linda Bojmar, John Jyazbek, Arnaub Chatterjee, David Lyden, Kenneth H. Yu, and David Paul Kelsen. "Error-free, automated data integration of exosome cargo protein data with extensive clinical data in an ongoing, multi-omic translational research study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16743-e16743. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16743.

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e16743 Background: Major advances in understanding the biology of cancer have come from genomic analysis of tumor and normal tissue. Integrating extensive patient-related data with deep analysis of omic data is crucial to informing omic data interpretation. Currently, such integrations are a highly manual, asynchronous, and costly process as well as error-prone and time-consuming. To develop new blood assays that may detect very early stage PDAC, a multi-omic investigation with deep clinical annotation is needed. Using pilot data from an on-going study, we test a new platform allowing automated error-free integration of an extensive clinical database with extensive omic data. Methods: Demographic, clinical, family pedigree and pathology data were collected on the Rave EDC platform. Exosomes were purified from 46 plasma samples from 14 controls and 24 PDAC patients and cargo proteins were quantified via SILAC. The Rave Omics platform was used to ingest and integrate clinical and omic data, run quality checks and generate integrated clinical-omic datasets. Data fidelity was validated by systematically computing differences between corresponding values in the source flies with those present in the extracted data object (integrated data). The root mean squared error (RMSE) was calculated for numeric values in each sample. Additional validation was conducted by manual inspection to ascertain data integrity. Results: We demonstrated automatic integration, without human intervention, of a subset of the clinical data and all available SILAC data into an analysis-ready data object. Data transfer was completely faithful, with 100% concordance between the source and the integrated data without loss of features. All proteins (n = 1515) and clinical variables (n = 64) were imported. Their nomenclature and corresponding sample values (n = 69690) and clinical values (n = 2432) matched exactly between datasets. In all samples, the RMSE was exactly zero, indicating no deviation between data sources. Conclusions: We demonstrated that automatic, efficient, and reliable integration of clinical-omic data is achievable during an in-flight PDAC trial. Automatic exploratory analytics supporting biomarker discovery are currently being used to uncover associations between omic and clinical features. The Rave Omics platform is disease-agnostic and we plan to expand to trials of varying size, indication, and completion status where systematic, automated integration of clinical and (multi)omic data is needed.

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Sánchez-Vidaña, Dalinda Isabel, Rahim Rajwani, and Man-Sau Wong. "The Use of Omic Technologies Applied to Traditional Chinese Medicine Research." Evidence-Based Complementary and Alternative Medicine 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/6359730.

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Natural products represent one of the most important reservoirs of structural and chemical diversity for the generation of leads in the drug development process. A growing number of researchers have shown interest in the development of drugs based on Chinese herbs. In this review, the use and potential of omic technologies as powerful tools in the modernization of traditional Chinese medicine are discussed. The analytical combination from each omic approach is crucial for understanding the working mechanisms of cells, tissues, organs, and organisms as well as the mechanisms of disease. Gradually, omic approaches have been introduced in every stage of the drug development process to generate high-quality Chinese medicine-based drugs. Finally, the future picture of the use of omic technologies is a promising tool and arena for further improvement in the modernization of traditional Chinese medicine.

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Daliri, Eric Banan-Mwine, Fred Kwame Ofosu, Ramachandran Chelliah, Byong H. Lee, and Deog-Hwan Oh. "Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies." Biomolecules 11, no. 2 (February 17, 2021): 300. http://dx.doi.org/10.3390/biom11020300.

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The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host–microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies.

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Zou, Xicong, Xuesen Zhao, Guo Li, Zengqiang Li, Zhenjiang Hu, and Tao Sun. "An on-machine error compensation method for an ultra-precision turning machine." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 5 (September 13, 2017): 1608–13. http://dx.doi.org/10.1177/0954405417731471.

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On-machine error compensation (OMEC) is efficient at improving machining accuracy without increasing extra manufacturing cost, and involves the on-machine measurement (OMM) of machining accuracy and modification of program code based on the measurement results. As an excellent OMM technique, chromatic confocal sensing allows for the rapid development of accurate and reliable error compensation technique. The present study integrated a non-contact chromatic confocal probe into an ultra-precision machine for OMM and OMEC of machined components. First, the configuration and effectiveness of the OMM system were briefly described, and the relevant OMEC method was presented. With the OMM result, error compensation software was then developed to automatically generate a modified program code for error compensation. Finally, a series of cutting experiments were performed to verify the validity of the proposed OMEC method. The experimental results demonstrate that the proposed error compensation method is reliable and considerably improves the form error of machined components.

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Shah, Tariq, Jinsong Xu, Xiling Zou, Yong Cheng, Mubasher Nasir, and Xuekun Zhang. "Omics Approaches for Engineering Wheat Production under Abiotic Stresses." International Journal of Molecular Sciences 19, no. 8 (August 14, 2018): 2390. http://dx.doi.org/10.3390/ijms19082390.

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Abiotic stresses greatly influenced wheat productivity executed by environmental factors such as drought, salt, water submergence and heavy metals. The effective management at the molecular level is mandatory for a thorough understanding of plant response to abiotic stress. Understanding the molecular mechanism of stress tolerance is complex and requires information at the omic level. In the areas of genomics, transcriptomics and proteomics enormous progress has been made in the omics field. The rising field of ionomics is also being utilized for examining abiotic stress resilience in wheat. Omic approaches produce a huge amount of data and sufficient developments in computational tools have been accomplished for efficient analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. Though, the incorporation of omic-scale data to address complex genetic qualities and physiological inquiries is as yet a challenge. In this review, we have reported advances in omic tools in the perspective of conventional and present day approaches being utilized to dismember abiotic stress tolerance in wheat. Attention was given to methodologies, for example, quantitative trait loci (QTL), genome-wide association studies (GWAS) and genomic selection (GS). Comparative genomics and candidate genes methodologies are additionally talked about considering the identification of potential genomic loci, genes and biochemical pathways engaged with stress resilience in wheat. This review additionally gives an extensive list of accessible online omic assets for wheat and its effective use. We have additionally addressed the significance of genomics in the integrated approach and perceived high-throughput multi-dimensional phenotyping as a significant restricting component for the enhancement of abiotic stress resistance in wheat.

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Prabakaran, Sudhakaran. "Omic profiling of melanoma evolution." Science Signaling 8, no. 397 (October 6, 2015): ec285-ec285. http://dx.doi.org/10.1126/scisignal.aad5259.

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Zhang, Fugen. "OMEC LS800 Laser Particle Sizer." China Particuology 1, no. 2 (June 2003): 91–92. http://dx.doi.org/10.1016/s1672-2515(07)60116-x.

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Dick, Gregory J., and Phyllis Lam. "Omic Approaches to Microbial Geochemistry." Elements 11, no. 6 (November 30, 2015): 403–8. http://dx.doi.org/10.2113/gselements.11.6.403.

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Ceglia, Nicholas, Yu Liu, Siwei Chen, Forest Agostinelli, Kristin Eckel-Mahan, Paolo Sassone-Corsi, and Pierre Baldi. "CircadiOmics: circadian omic web portal." Nucleic Acids Research 46, W1 (June 15, 2018): W157—W162. http://dx.doi.org/10.1093/nar/gky441.

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Asprer, J. "An excitingly predictable 'omic future." Development 139, no. 20 (September 18, 2012): 3675–76. http://dx.doi.org/10.1242/dev.088450.

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Quackenbush, John. "Data standards for 'omic' science." Nature Biotechnology 22, no. 5 (May 2004): 613–14. http://dx.doi.org/10.1038/nbt0504-613.

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Jackson, Heather, Stephanie Menikou, Shea Hamilton, Andrew McArdle, Chisato Shimizu, Rachel Galassini, Honglei Huang, et al. "Kawasaki Disease Patient Stratification and Pathway Analysis Based on Host Transcriptomic and Proteomic Profiles." International Journal of Molecular Sciences 22, no. 11 (May 26, 2021): 5655. http://dx.doi.org/10.3390/ijms22115655.

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The aetiology of Kawasaki disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host ‘omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple ‘omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering, and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infections at the transcriptomic and proteomic levels through comparison of ‘omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both ‘omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both ‘omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.

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Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (March 4, 2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.

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Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.

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Kuurne, Kaisa. "Kuinka ihmiselämä mullistuu hiljaisesti omien silmiemme alla." Janus Sosiaalipolitiikan ja sosiaalityön tutkimuksen aikakauslehti 27, no. 1 (March 8, 2019): 94–97. http://dx.doi.org/10.30668/janus.69095.

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Badimon, Lina, Guiomar Mendieta, Soumaya Ben-Aicha, and Gemma Vilahur. "Post-Genomic Methodologies and Preclinical Animal Models: Chances for the Translation of Cardioprotection to the Clinic." International Journal of Molecular Sciences 20, no. 3 (January 25, 2019): 514. http://dx.doi.org/10.3390/ijms20030514.

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Although many cardioprotective strategies have demonstrated benefits in animal models of myocardial infarction, they have failed to demonstrate cardioprotection in the clinical setting highlighting that new therapeutic target and treatment strategies aimed at reducing infarct size are urgently needed. Completion of the Human Genome Project in 2001 fostered the post-genomic research era with the consequent development of high-throughput “omics” platforms including transcriptomics, proteomics, and metabolomics. Implementation of these holistic approaches within the field of cardioprotection has enlarged our understanding of ischemia/reperfusion injury with each approach capturing a different angle of the global picture of the disease. It has also contributed to identify potential prognostic/diagnostic biomarkers and discover novel molecular therapeutic targets. In this latter regard, “omic” data analysis in the setting of ischemic conditioning has allowed depicting potential therapeutic candidates, including non-coding RNAs and molecular chaperones, amenable to pharmacological development. Such discoveries must be tested and validated in a relevant and reliable myocardial infarction animal model before moving towards the clinical setting. Moreover, efforts should also focus on integrating all “omic” datasets rather than working exclusively on a single “omic” approach. In the following manuscript, we will discuss the power of implementing “omic” approaches in preclinical animal models to identify novel molecular targets for cardioprotection of interest for drug development.

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Yazdani, Shahdadfar, Jackson, and Utheim. "A Hyaluronan Hydrogel Scaffold for Culture of Human Oral Mucosal Epithelial Cells in Limbal Stem-Cell Therapy." Bioengineering 6, no. 4 (October 23, 2019): 97. http://dx.doi.org/10.3390/bioengineering6040097.

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Hyaluronan (HA), a major component of the extracellular matrix, plays a key role in cell proliferation, growth, survival, polarization and differentiation. We investigated the optimization of a HA hydrogel scaffold for culture of human oral mucosal epithelial cells (OMECs) for potential application in limbal stem cell therapy. The effect of the optimized scaffold on OMEC cell sheet morphology, cell metabolic activity and expression of genes associated with stemness, adherence and cell damage was studied. The results indicate that HA hydrogels crosslinked with polyethylene glycol diacrylate (PEGDA) failed to support OMEC attachment and growth. However, HA hydrogel scaffolds dried for three days and coated with 1 mg/mL collagen IV produced a full OMEC sheet. Cell morphology was comparable to control after three weeks culture, maintaining 76% metabolic activity. Of apoptosis-related genes, the pro-apoptotic markers CASP3 and BAX2 were upregulated and downregulated, respectively, compared to control whereas the anti-apoptotic marker BCL2 was downregulated. The expression level of stemness genes ΔNp63α and ABCG2 was significantly higher than control. Genes associated with improved scar-less wound healing (integrin-V) and protection of the ocular surface (cadherin-1) had ~3-fold increased expression. These data suggest that our optimized HA-hydrogel scaffold could enhance culture of OMEC cell sheets for use in ocular reconstruction.

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Chaddad, Ahmad, Paul Daniel, Siham Sabri, Christian Desrosiers, and Bassam Abdulkarim. "Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma." Cancers 11, no. 8 (August 10, 2019): 1148. http://dx.doi.org/10.3390/cancers11081148.

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Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10−5). We posit that multi-omic models using the limited set of ‘omic’ features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients.

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Mehta, Bhaven K., Monica E. Espinoza, Monique Hinchcliff, and Michael L. Whitfield. "Molecular “omic” signatures in systemic sclerosis." European Journal of Rheumatology 7, no. -3 (October 28, 2020): 173–80. http://dx.doi.org/10.5152/eurjrheum.2020.19192.

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Sussman, Hillary E. "Omic omen for environmental health research." Drug Discovery Today 9, no. 2 (January 2004): 56. http://dx.doi.org/10.1016/s1359-6446(03)02967-2.

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Loughlin, Michael F. "Using ‘omic’ technology to targetHelicobacter pylori." Expert Opinion on Drug Discovery 2, no. 8 (August 2007): 1041–51. http://dx.doi.org/10.1517/17460441.2.8.1041.

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Calvete, Juan J., and Gilberto B. Domont. "Omic technologies to fight the neglect." Journal of Proteomics 74, no. 9 (August 2011): 1483–84. http://dx.doi.org/10.1016/j.jprot.2011.07.004.

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Boekel, Jorrit, John M. Chilton, Ira R. Cooke, Peter L. Horvatovich, Pratik D. Jagtap, Lukas Käll, Janne Lehtiö, Pieter Lukasse, Perry D. Moerland, and Timothy J. Griffin. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (February 2015): 137–39. http://dx.doi.org/10.1038/nbt.3134.

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Zhu, Heng, and Michael Snyder. "‘Omic’ approaches for unraveling signaling networks." Current Opinion in Cell Biology 14, no. 2 (April 2002): 173–79. http://dx.doi.org/10.1016/s0955-0674(02)00315-0.

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Astin, J. "Caenorhabditis elegans functional genomics: Omic resonance." Briefings in Functional Genomics and Proteomics 3, no. 1 (January 1, 2004): 26–34. http://dx.doi.org/10.1093/bfgp/3.1.26.

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Simons, Kai. "How Can Omic Science be Improved?" PROTEOMICS 18, no. 5-6 (March 2018): 1800039. http://dx.doi.org/10.1002/pmic.201800039.

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Goodsell, Kim. "Gaming Aging in the “Omic” Era." Circulation Research 122, no. 5 (March 2, 2018): 646–48. http://dx.doi.org/10.1161/circresaha.118.312703.

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Evans, Glen A. "Designer science and the “omic” revolution." Nature Biotechnology 18, no. 2 (February 2000): 127. http://dx.doi.org/10.1038/72480.

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Menke, Anne M., and James B. Sprague. "OMIC workshop: Documentation of ophthalmic care." Journal of American Association for Pediatric Ophthalmology and Strabismus 11, no. 1 (February 2007): 107. http://dx.doi.org/10.1016/j.jaapos.2006.11.055.

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Krittanawong, Chayakrit. "Big Data Analytics, the Microbiome, Host-omic and Bug-omic Data and Risk for Cardiovascular Disease." Heart, Lung and Circulation 27, no. 3 (March 2018): e26-e27. http://dx.doi.org/10.1016/j.hlc.2017.07.012.

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Korobov, A. P., and E. V. Bykova. "Organic microelement complex in feeding of cows in the production of iodized milk." Kormlenie sel'skohozjajstvennyh zhivotnyh i kormoproizvodstvo (Feeding of agricultural animals and feed production), no. 6 (June 1, 2021): 24–38. http://dx.doi.org/10.33920/sel-05-2106-03.

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The optimal level of mineral nutrition of animals is of great importance in the organization of complete feeding. Micromineral feeding of cattle, pigs and poultry has recently received more and more attention. A significant contribution to the solution of this issue is made by an organic microelement complex based on L-aspartic amino acid produced by JSC “Bioamide” under the trademark OMEC-7M. The purpose of the research was to study the effect of the organic microelement complex based on the L-aspartic amino acid OMEC-7M and organic iodine OMEC-J on the milk productivity and composition of cow milk. In order to conduct scientific and economic experiments, groups of cows have been formed according to the principle of analogous pairs taking into account age, live weight, date of the last calving, productivity and physiological state. Live weight of cows was 500–550 kg with a yield of 5000-6000 kg of milk per lactation. The conditions of feeding and housing the animals were the same, with the exception of the studied factors. Studies have been conducted to determine the effect of organic trace elements Cu, Mn, Zn, Fe, Co and Se in the form of asparaginates and organic J on the milk productivity of cows, the composition of milk and the content of iodine in it, the morphological and biochemical composition of blood, the content of the rumen, the digestibility and use of feed nutrients compared to the inorganic salts of these trace elements. As a result of four scientific and economic experiments the effectiveness of the use of the organic microelement complex based on the L-aspartic amino acid OMEC-7M and organic iodine OMEC-J in the feeding of cows in the production of iodized milk has been proved.

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Chorna, Nataliya, and Filipa Godoy-Vitorino. "A Protocol for the Multi-Omic Integration of Cervical Microbiota and Urine Metabolomics to Understand Human Papillomavirus (HPV)-Driven Dysbiosis." Biomedicines 8, no. 4 (April 8, 2020): 81. http://dx.doi.org/10.3390/biomedicines8040081.

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The multi-omic integration of microbiota data with metabolomics has gained popularity. This protocol is based on a human multi-omics study, integrating cervicovaginal microbiota, HPV status and neoplasia, with urinary metabolites. Indeed, to understand the biology of the infections and to develop adequate interventions for cervical cancer prevention, studies are needed to characterize in detail the cervical microbiota and understand the systemic metabolome. This article is a detailed protocol for the multi-omic integration of cervical microbiota and urine metabolome to shed light on the systemic effects of cervical dysbioses associated with Human Papillomavirus (HPV) infections. This methods article suggests detailed sample collection and laboratory processes of metabolomics, DNA extraction for microbiota, HPV typing, and the bioinformatic analyses of the data, both to characterize the metabolome, the microbiota, and joint multi-omic analyses, useful for the development of new point-of-care diagnostic tests based on these approaches.

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Zanfardino, Pane, Mirabelli, Salvatore, and Franzese. "TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review." International Journal of Molecular Sciences 20, no. 23 (November 29, 2019): 6033. http://dx.doi.org/10.3390/ijms20236033.

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In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular ‘omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular ‘omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular ‘omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

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Mäyrä, Ilkka. "Audiovisuaalista mediakulttuuria verkko-opintoina." Aikuiskasvatus 20, no. 2 (May 15, 2000): 151–56. http://dx.doi.org/10.33336/aik.93282.

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Myös verkkopohjaisessa opiskelussa sosiaalinen vuorovaikutus on tärkeä osa oppimista sekä katalysaattorina että omien ajatusten peilauskenttänä, kirjoittaa TYT:n projektipäällikkö Ilkka Mäyrä esitellessään mediakulttuurin perusopintojen toteutusta verkko-opiskeluna.

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Hernández-Lemus, Enrique, Helena Reyes-Gopar, Jesús Espinal-Enríquez, and Soledad Ochoa. "The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook." Genes 10, no. 11 (October 30, 2019): 865. http://dx.doi.org/10.3390/genes10110865.

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Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

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Saari, Tiina. "Työssä kehittyminen tietotyöntekijän oikeutena ja velvollisuutena." Aikuiskasvatus 33, no. 2 (May 15, 2013): 95–106. http://dx.doi.org/10.33336/aik.94031.

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Onko työssä kehittyminen työntekijän oikeus vai työnantajan asettama velvollisuus? Tietotyössä se on osa psykologista sopimusta, johon kuuluu kaksisuuntaisuus ja vastavuoroisuus. Yleensä työntekijä kuitenkin itse huolehtii omien oikeuksiensa toteutumisesta, kuten lisäkouluttautumisesta.

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Miller, David T., Isidro Cortés-Ciriano, Nischalan Pillay, Angela C. Hirbe, Matija Snuderl, Marilyn M. Bui, Katherine Piculell, et al. "Genomics of MPNST (GeM) Consortium: Rationale and Study Design for Multi-Omic Characterization of NF1-Associated and Sporadic MPNSTs." Genes 11, no. 4 (April 2, 2020): 387. http://dx.doi.org/10.3390/genes11040387.

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The Genomics of Malignant Peripheral Nerve Sheath Tumor (GeM) Consortium is an international collaboration focusing on multi-omic analysis of malignant peripheral nerve sheath tumors (MPNSTs), the most aggressive tumor associated with neurofibromatosis type 1 (NF1). Here we present a summary of current knowledge gaps, a description of our consortium and the cohort we have assembled, and an overview of our plans for multi-omic analysis of these tumors. We propose that our analysis will lead to a better understanding of the order and timing of genetic events related to MPNST initiation and progression. Our ten institutions have assembled 96 fresh frozen NF1-related (63%) and sporadic MPNST specimens from 86 subjects with corresponding clinical and pathological data. Clinical data have been collected as part of the International MPNST Registry. We will characterize these tumors with bulk whole genome sequencing, RNAseq, and DNA methylation profiling. In addition, we will perform multiregional analysis and temporal sampling, with the same methodologies, on a subset of nine subjects with NF1-related MPNSTs to assess tumor heterogeneity and cancer evolution. Subsequent multi-omic analyses of additional archival specimens will include deep exome sequencing (500×) and high density copy number arrays for both validation of results based on fresh frozen tumors, and to assess further tumor heterogeneity and evolution. Digital pathology images are being collected in a cloud-based platform for consensus review. The result of these efforts will be the largest MPNST multi-omic dataset with correlated clinical and pathological information ever assembled.

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Kristensen, Torsten N., Kamilla S. Pedersen, Cornelis J. Vermeulen, and Volker Loeschcke. "Research on inbreeding in the ‘omic’ era." Trends in Ecology & Evolution 25, no. 1 (January 2010): 44–52. http://dx.doi.org/10.1016/j.tree.2009.06.014.

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Ulrich-Merzenich, G., H. Zeitler, D. Jobst, D. Panek, H. Vetter, and H. Wagner. "Application of the “-Omic-” technologies in phytomedicine." Phytomedicine 14, no. 1 (January 2007): 70–82. http://dx.doi.org/10.1016/j.phymed.2006.11.011.

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Grennan, Aleel K. "Abiotic Stress in Rice. An “Omic” Approach." Plant Physiology 140, no. 4 (April 2006): 1139–41. http://dx.doi.org/10.1104/pp.104.900188.

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Rohatynskyj, Marta. "The Larger Context of Omie Sex Affiliation." Man 25, no. 3 (September 1990): 434. http://dx.doi.org/10.2307/2803712.

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Journal articles on the topic 'OMIEC'

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