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Relevant bibliographies by topics / Omic data

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Journal articles

Academic literature on the topic 'Omic data'

Author: Grafiati

Published: 19 April 2025

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Journal articles on the topic "Omic data"

1

Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, et al. "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 (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 automate

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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 (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 tha

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3

Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (2019): 3348–56. http://dx.doi.org/10.1093/bioinformatics/btz058.

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Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data impu

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Canela, Núria Anela. "A pioneering multi-omics data platform sheds light on the understanding of biological systems." Project Repository Journal 20, no. 1 (2024): 20–23. http://dx.doi.org/10.54050/prj2021863.

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A pioneering multi-omics data platform sheds light on the understanding of biological systems The GLOMICAVE project has developed an innovative multi-omics data analysis digital platform, relying on big data analytics and artificial intelligence and using large-scale publicly available and experimental omic datasets. The project aimed to maximise the utility of omic data at a massive level and discover new links between animal and vegetable genotype and phenotype, understanding biological systems as a whole.

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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 work

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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 th

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7

Escriba-Montagut, Xavier, Yannick Marcon, Augusto Anguita-Ruiz, et al. "Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform." PLOS Computational Biology 20, no. 12 (2024): e1012626. https://doi.org/10.1371/journal.pcbi.1012626.

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The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide r

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Meunier, Lea, Guillaume Appe, Abdelkader Behdenna, et al. "Abstract 6209: From data disparity to data harmony: A comprehensive pan-cancer omics data collection." Cancer Research 84, no. 6_Supplement (2024): 6209. http://dx.doi.org/10.1158/1538-7445.am2024-6209.

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Abstract In cancer research, the exponential growth of omics datasets offers a significant opportunity for scientific advancement. However, challenges such as the lack of uniform standards, in both clinical and omic data, hinder the effective utilization of these datasets, thus impeding our understanding of cancer biology and the development of innovative therapeutic approaches.Addressing these challenges, we have created a novel collection of pan-cancer omics datasets with extensive clinical data harmonization and consistent omic data normalization.Here, we focused on patient-derived gene exp

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9

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

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10

Boekel, Jorrit, John M. Chilton, Ira R. Cooke, et al. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (2015): 137–39. http://dx.doi.org/10.1038/nbt.3134.

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