Auswahl der wissenschaftlichen Literatur zum Thema „Multiomics analysis“
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Zeitschriftenartikel zum Thema "Multiomics analysis":
Lee, Jeongwoo, Do Young Hyeon und Daehee Hwang. „Single-cell multiomics: technologies and data analysis methods“. Experimental & Molecular Medicine 52, Nr. 9 (September 2020): 1428–42. http://dx.doi.org/10.1038/s12276-020-0420-2.
Dai, Ling-Yun, Rong Zhu und Juan Wang. „Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis“. Complexity 2020 (16.11.2020): 1–10. http://dx.doi.org/10.1155/2020/3917812.
Wang, Tzu-Hao, Cheng-Yang Lee, Tzong-Yi Lee, Hsien-Da Huang, Justin Bo-Kai Hsu und Tzu-Hao Chang. „Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion“. Cancers 13, Nr. 11 (21.05.2021): 2528. http://dx.doi.org/10.3390/cancers13112528.
Boroń, Dariusz, Nikola Zmarzły, Magdalena Wierzbik-Strońska, Joanna Rosińczuk, Paweł Mieszczański und Beniamin Oskar Grabarek. „Recent Multiomics Approaches in Endometrial Cancer“. International Journal of Molecular Sciences 23, Nr. 3 (22.01.2022): 1237. http://dx.doi.org/10.3390/ijms23031237.
Rotroff, Daniel M., und Alison A. Motsinger-Reif. „Embracing Integrative Multiomics Approaches“. International Journal of Genomics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/1715985.
Nassar, Sam F., Khadir Raddassi und Terence Wu. „Single-Cell Multiomics Analysis for Drug Discovery“. Metabolites 11, Nr. 11 (25.10.2021): 729. http://dx.doi.org/10.3390/metabo11110729.
Perkel, Jeffrey M. „Single-cell analysis enters the multiomics age“. Nature 595, Nr. 7868 (19.07.2021): 614–16. http://dx.doi.org/10.1038/d41586-021-01994-w.
Marshall, John L., Beth N. Peshkin, Takayuki Yoshino, Jakob Vowinckel, Håvard E. Danielsen, Gerry Melino, Ioannis Tsamardinos et al. „The Essentials of Multiomics“. Oncologist 27, Nr. 4 (22.02.2022): 272–84. http://dx.doi.org/10.1093/oncolo/oyab048.
Campuzano, Susana, Rodrigo Barderas, Paloma Yáñez-Sedeño und José M. Pingarrón. „Electrochemical biosensing to assist multiomics analysis in precision medicine“. Current Opinion in Electrochemistry 28 (August 2021): 100703. http://dx.doi.org/10.1016/j.coelec.2021.100703.
Xing, Lu, Tao Wu, Li Yu, Nian Zhou, Zhao Zhang, Yunjing Pu, Jinnan Wu und Hong Shu. „Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis“. Mediators of Inflammation 2022 (23.09.2022): 1–25. http://dx.doi.org/10.1155/2022/7731082.
Dissertationen zum Thema "Multiomics analysis":
Richerd, Mathilde. „Développement d'un système de microfluidique de gouttes pour l'analyse d'échantillons complexes“. Thesis, Université Paris sciences et lettres, 2021. http://www.theses.fr/2021UPSLS051.
Droplet microfluidics is a technology with a huge potential for miniaturization and automation of conventional methods in bioanalysis. Indeed, droplet microfluidics has many functionalities, such as merging, sorting and cell encapsulation, that can reproduce manipulations in standard protocols in bioanalysis on very small volumes with advantages such as a low samples and reagents consumption and reduction of analysis duration. During this thesis, we developed protocols for two types of biological analysis, using magnetic particles manipulation in 100 nL droplets.The first project is about single cell multiomics analysis. We implemented a droplet microfluidics protocol for the separation of mRNA and DNA from complex samples: from a mix of pre-purified RNA and DNA to a suspension of less than ten cells. At each steps, the droplet system performances were evaluated and the results were compared to extractions made in tubes in a non-microfluidics way. The results are very promising since they demonstrate for the first time in such a system the sequential separation of DNA and RNA from cell lysate or a few cells encapsulated and lysed in droplets.The second project is about the sample preparation in droplet for MALDI-TOF mass spectrometry analysis. This project was built in collaboration researchers from CEA Saclay. They have shown that it is very interesting to deposit samples in droplets on MALDI plates to increase the detection sensitivity thanks to a focusing effect. We have implemented on an original integrated droplet microfluidic approach for protein analysis by MALDI-TOF MS: from in drop sample preparation by supported enzymatic digestion to on plate deposition and peptides local pre-concentration. Development was done with lysozyme as a model protein and thanks to our in-drop digestion and deposition protocol we identified this protein by mass fingerprint from 200 fmol protein with a good reliability
Bodily, Weston Reed. „Integrative Analysis to Evaluate Similarity Between BRCAness Tumors and BRCA Tumors“. BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6800.
Backman, Mattias [Verfasser], und Eckhard [Akademischer Betreuer] Wolf. „Bioinformatic analysis of multiomic data from the Munich MIDY Pig biobank / Mattias Backman ; Betreuer: Eckhard Wolf“. München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/121985204X/34.
Proust, Lucas. „Analyse multiomique des peptides d’extraits de levure et de leurs impacts fonctionnels sur Streptococcus thermophilus“. Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLA032.
Lactic acid bacteria are widely used as starters in dairy industry. They are generally produced in complex fermentation media containing a wide array of nutrients that can be provided by yeast extracts (YEs). The main goal of this thesis project, involving two industrial partners, Lesaffre and Sacco, as well as INRA, was to investigate the effect of the peptide fraction of two YEs (YE1 and YE2) on an industrial Streptococcus thermophilus strain, a major lactic acid starter. The underlying hypothesis of this whole project was that YE peptides could have a role in nutrition but also regulate cellular functions of technological relevance. In order to explore this question, a two-step strategy was elaborated: i) mass spectrometry characterization (peptidomics) of both YE peptide fractions and time course analysis of their relative abundance during the growth in bioreactors of S. thermophilus, and ii) parallel time course analysis of the strain transcriptome and proteome. The final objective was to cross these two levels of information in order to correlate differences of peptide content with differentially activated systems related to technological performances.YE peptidome characterization and kinetic analysis first required an important methodological development. It eventually resulted in a complete analytical tool that combines high throughput peptidomic analysis as well as bioinformatic and statistical data processing. This powerful tool was able to identify around 4,000 different peptides in both YEs. Then, the time course analysis also clarified the in vivo substrate specificities of the oligopeptide transport system of the bacterium (Ami). A peptide positive net charge notably turned out to be the leading factor governing peptide transport. In addition to this semi-quantitative approach, quantitative analyses were carried out on YE peptide fractions (differential HPLC analyses of amino acids before and after sample hydrolysis). They notably revealed significant differences in oligopeptides content between both YEs.Meanwhile, genome scale transcriptomic and proteomic analyses performed during the strain growth highlighted two significant events. The first one concerns the overexpression in YE1 of a quorum sensing-based genetic locus that uses a pheromone peptide as molecular signal. The second event relates to several biosynthesis pathways (amino acids and purines) that were differentially affected by both YEs. These dynamics could result from differences in peptide content between both substrates. In particular, some pathways could have been differentially modulated by central regulators such as CodY, whose activity is correlated to the medium peptide richness, or YebC, a CodY-like regulator whose functional link with CodY is still unknown in S. thermophilus. All these results open avenues for a better understanding of the interplay between peptides and bacterial metabolism. In the future, this whole approach could lead to the identification of performance biomarkers in YEs, which in turns may eventually translate into the conception of new customized products granting high technological performances to dairy starters
Gressel, Saskia. „Multi-omics analysis of transcription kinetics in human cells“. Doctoral thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0003-C183-E.
Buchteile zum Thema "Multiomics analysis":
Taguchi, Y.-h. „Multiomics Data Analysis Using Tensor Decomposition Based Unsupervised Feature Extraction“. In Intelligent Computing Theories and Application, 565–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26763-6_54.
Taguchi, Y. H. „Multiomics Data Analysis of Cancers Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction“. In Studies in Big Data, 1–17. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9158-4_1.
Ahrendt, Steven R., Stephen J. Mondo, Sajeet Haridas und Igor V. Grigoriev. „MycoCosm, the JGI’s Fungal Genome Portal for Comparative Genomic and Multiomics Data Analyses“. In Microbial Environmental Genomics (MEG), 271–91. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2871-3_14.
González, Juan R., und Alejandro Cáceres. „Multiomic data analysis“. In Omic Association Studies with R and Bioconductor, 315–52. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429440557-11.
Hassan, Muhammad Jawad, Muhammad Faheem und Sabba Mehmood. „Emerging OMICS and Genetic Disease“. In Omics Technologies for Clinical Diagnosis and Gene Therapy: Medical Applications in Human Genetics, 93–113. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815079517122010010.
Marín de Mas, Igor. „Multiomic Data Integration and Analysis via Model-Driven Approaches“. In Comprehensive Analytical Chemistry, 447–76. Elsevier, 2018. http://dx.doi.org/10.1016/bs.coac.2018.07.005.
Bangert, Patrick, Nandha Kumar Balasubramaniam, Carol E. Parker und Christoph H. Borchers. „Pattern Recognition for Mass-Spectrometry-Based Proteomics“. In Biomedical Engineering. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108422.
Xia, Yinglin. „Correlation and association analyses in microbiome study integrating multiomics in health and disease“. In Progress in Molecular Biology and Translational Science, 309–491. Elsevier, 2020. http://dx.doi.org/10.1016/bs.pmbts.2020.04.003.
Konferenzberichte zum Thema "Multiomics analysis":
Hristova, V., A. Watson, M. Glover, J. Wang, B. Angermann, S. Ashenden, A. Bornot et al. „S90 Comprehensive multiomics analysis demonstrates surfactant dysregulation in COPD“. In British Thoracic Society Winter Meeting 2021 Online, Wednesday 24 to Friday 26 November 2021, Programme and Abstracts. BMJ Publishing Group Ltd and British Thoracic Society, 2021. http://dx.doi.org/10.1136/thorax-2021-btsabstracts.96.
Gardner, Lois, Dominic G. Rothwell, Caroline Dive, Kostas Kostarelos und Marilena Hadjidemetriou. „Abstract 568: Nanonets for multiomics blood analysis and cancer biomarker discovery“. In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-568.
Zounemat Kermani, nazanin, John Busby, Kai Sun, Ioannis Pandis, Gabrielle Gainsborough, Yike Guo, Ian Adcock, Tim Hardman und Liam Heaney. „A data management and analysis platform for RASP-UK multiomics clinical datasets“. In ERS International Congress 2021 abstracts. European Respiratory Society, 2021. http://dx.doi.org/10.1183/13993003.congress-2021.oa4058.
Sen, Sidharth, Tyler McCubbin, Shannon K. King, Laura A. Greeley, Shuai Zeng, Cheyenne Baker, Rachel Mertz et al. „A multiomics discriminatory analysis approach to identify drought-related signatures in maize nodal roots“. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313382.
Mattie, Mike, Regis Perbost, Sarah Turcan, Corinne Danan, Frederick Locke, Sattva Neelapu, David Miklos et al. „1454 Multiomics and multimodal analysis approach to construct a diffuse large B cell lymphoma atlas of tumor microenvironment for predictive modeling“. In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1454.
Chen, Kezhong, Airong Yang, Shuangxiu Wu, Yuntao Nie, Haifeng Shen, Jian Bai, Lin Wu, Fan Yang und Jun Wang. „Abstract 618: Spatio-temporal multiomics analysis reveals distinct molecular features in recurrent stage I non-small cell lung cancers after R0 tumor resection“. In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-618.
Carl, Sarah, Juana Flores-Candia, Jeremy Staub, Jasmin D’Andrea, Brittney Ruedlinger, Kate Shapland, Jared Ehrhart und Soner Altiok. „1438 Integrative analysis of single cell multiomics data using deep learning to identify immune related biomarkers in a patient derived 3D ex vivo tumoroid platform“. In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1438.
Ooi, Aik, Pedro Mendez, Dalia Dhingra, Nigel Beard und David Ruff. „Abstract 5910: Single-cell multiomic analysis of SNV, CNV, and protein expression“. In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-5910.
Corselli, Mirko, Suraj Saksena, Margaret Nakamoto, Woodrow E. Lomas, Ian Taylor und Pratip K. Chattopadhyay. „Abstract 2166: Deep characterization ofin vitrochronically stimulated T cells via single-cell multiomic analysis“. In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-2166.
Bareche, Yacine, David Venet, Philippe Aftimos, Michail Ignatiadis, Martine Piccart, Francoise Rothe und Christos Sotiriou. „Abstract 3698: Unraveling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis“. In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-3698.