Gotowa bibliografia na temat „Omic network inference”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Omic network inference”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Omic network inference"
Nagpal, Sunil, Rashmi Singh, Deepak Yadav i Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks". Nucleic Acids Research 48, W1 (27.04.2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.
Pełny tekst źródłaDohlman, Anders B., i Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference". Experimental Biology and Medicine 244, nr 6 (16.03.2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.
Pełny tekst źródłaRamos, Susana Isabel, Zarmeen Mussa, Bruno Giotti, Alexander Tsankov i Nadejda Tsankova. "EPCO-25. MULTI-OMIC ANALYSIS OF THE GLIOBLASTOMA EPIGENOME AND TRANSCRIPTOME INFORMS OF MIGRATORY INTERNEURON-LIKE DEVELOPMENTAL REGULATORS". Neuro-Oncology 24, Supplement_7 (1.11.2022): vii121. http://dx.doi.org/10.1093/neuonc/noac209.460.
Pełny tekst źródłaGrund, Eric M., A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah i in. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis". Cancer Research 82, nr 12_Supplement (15.06.2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.
Pełny tekst źródłaNathasingh, Brandon, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W. Church i Yaoyu E. Wang. "Abstract LB181: Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model". Cancer Research 83, nr 8_Supplement (14.04.2023): LB181. http://dx.doi.org/10.1158/1538-7445.am2023-lb181.
Pełny tekst źródłaYe, Qing, i Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets". Cells 12, nr 1 (26.12.2022): 101. http://dx.doi.org/10.3390/cells12010101.
Pełny tekst źródłaAlanis-Lobato, Gregorio, Thomas E. Bartlett, Qiulin Huang, Claire S. Simon, Afshan McCarthy, Kay Elder, Phil Snell, Leila Christie i Kathy K. Niakan. "MICA: a multi-omics method to predict gene regulatory networks in early human embryos". Life Science Alliance 7, nr 1 (25.10.2023): e202302415. http://dx.doi.org/10.26508/lsa.202302415.
Pełny tekst źródłaWang, Pei. "Network biology: Recent advances and challenges". Gene & Protein in Disease 1, nr 2 (6.10.2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.
Pełny tekst źródłaYan, Yan, Feng Jiang, Xinan Zhang i Tianhai Tian. "Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm". Entropy 24, nr 5 (13.05.2022): 693. http://dx.doi.org/10.3390/e24050693.
Pełny tekst źródłaBonnet, Eric, Laurence Calzone i Tom Michoel. "Integrative Multi-omics Module Network Inference with Lemon-Tree". PLOS Computational Biology 11, nr 2 (13.02.2015): e1003983. http://dx.doi.org/10.1371/journal.pcbi.1003983.
Pełny tekst źródłaRozprawy doktorskie na temat "Omic network inference"
Arsenteva, Polina. "Statistical modeling and analysis of radio-induced adverse effects based on in vitro and in vivo data". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCK074.
Pełny tekst źródłaIn this work we address the problem of adverse effects induced by radiotherapy on healthy tissues. The goal is to propose a mathematical framework to compare the effects of different irradiation modalities, to be able to ultimately choose those treatments that produce the minimal amounts of adverse effects for potential use in the clinical setting. The adverse effects are studied in the context of two types of data: in terms of the in vitro omic response of human endothelial cells, and in terms of the adverse effects observed on mice in the framework of in vivo experiments. In the in vitro setting, we encounter the problem of extracting key information from complex temporal data that cannot be treated with the methods available in literature. We model the radio-induced fold change, the object that encodes the difference in the effect of two experimental conditions, in the way that allows to take into account the uncertainties of measurements as well as the correlations between the observed entities. We construct a distance, with a further generalization to a dissimilarity measure, allowing to compare the fold changes in terms of all the important statistical properties. Finally, we propose a computationally efficient algorithm performing clustering jointly with temporal alignment of the fold changes. The key features extracted through the latter are visualized using two types of network representations, for the purpose of facilitating biological interpretation. In the in vivo setting, the statistical challenge is to establish a predictive link between variables that, due to the specificities of the experimental design, can never be observed on the same animals. In the context of not having access to joint distributions, we leverage the additional information on the observed groups to infer the linear regression model. We propose two estimators of the regression parameters, one based on the method of moments and the other based on optimal transport, as well as the estimators for the confidence intervals based on the stratified bootstrap procedure
Wrzodek, Clemens [Verfasser]. "Inference and integration of biochemical networks with multilayered omics data / Clemens Wrzodek". München : Verlag Dr. Hut, 2013. http://d-nb.info/1042307652/34.
Pełny tekst źródłaVincent, Jonathan. "Inférence des réseaux de régulation de la synthèse des protéines de réserve du grain de blé tendre (Triticum aestivum L.) en réponse à l'approvisionnement en azote et en soufre". Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22485/document.
Pełny tekst źródłaGrain storage protein content and composition are the main determinants of bread wheat (Triticum aestivum L.) end-use value. Scaling laws governing grain protein composition according to grain nitrogen and sulfur content could be the outcome of a finely tuned regulation network. Although it was demonstrated that the main regulation of grain storage proteins accumulation occurs at the transcriptomic level in cereals, knowledge of the underlying molecular mechanisms is elusive. Moreover, the effects of nitrogen and sulfur on these mechanisms are unknown. The issue of skyrocketing data generation in research projects is addressed by developing high-throughput bioinformatics approaches. Extracting knowledge on from such massive amounts of data is therefore an important challenge. The work presented herein aims at elucidating regulatory networks involved in grain storage protein synthesis and their response to nitrogen and sulfur supply using a rule discovery approach. This approach was extended, implemented in the form of a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data. This platform allowed us to define different semantics in a comprehensive framework; each semantic having its own biological meaning, thus providing us with global informative networks. Spatiotemporal specificity of transcription factors expression was observed and particular attention was paid to their relationship with grain storage proteins in the inferred networks. The work initiated here opens up a field of innovative investigation to identify new targets for plant breeding and for an improved end-use value and nutritional quality of wheat in the context of inputs limitation. Further analyses should enhance the understanding of the control of grain protein composition and allow providing wheat adapted to specific uses or deficient in protein fractions responsible for gluten allergenicity and intolerance
Hulot, Audrey. "Analyses de données omiques : clustering et inférence de réseaux Female ponderal index at birth and idiopathic infertility". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL034.
Pełny tekst źródłaThe development of biological high-throughput technologies (next-generation sequencing and mass spectrometry) have provided researchers with a large amount of data, also known as -omics, that help better understand the biological processes.However, each source of data separately explains only a very small part of a given process. Linking the differents -omics sources between them should help us understand more of these processes.In this manuscript, we will focus on two approaches, clustering and network inference, applied to omics data.The first part of the manuscript presents three methodological developments on this topic. The first two methods are applicable in a situation where the data are heterogeneous.The first method is an algorithm for aggregating trees, in order to create a consensus out of a set of trees. The complexity of the process is sub-quadratic, allowing to use it on data leading to a great number of leaves in the trees. This algorithm is available in an R-package named mergeTrees on the CRAN.The second method deals with the integration data from trees and networks, by transforming these objects into distance matrices using cophenetic and shortest path distances, respectively. This method relies on Multidimensional Scaling and Multiple Factor Analysis and can be also used to build consensus trees or networks.Finally, we use the Gaussian Graphical Models setting and seek to estimate a graph, as well as communities in the graph, from several tables. This method is based on a combination of Stochastic Block Model, Latent Block Model and Graphical Lasso.The second part of the manuscript presents analyses conducted on transcriptomics and metagenomics data to identify targets to gain insight into the predisposition of Ankylosing Spondylitis
Griffin, Paula Jean. "Biological network models for inferring mechanism of action, characterizing cellular phenotypes, and predicting drug response". Thesis, 2016. https://hdl.handle.net/2144/14516.
Pełny tekst źródłaCzęści książek na temat "Omic network inference"
Lecca, Paola, Thanh-Phuong Nguyen, Corrado Priami i Paola Quaglia. "Network Inference from Time-Dependent Omics Data". W Methods in Molecular Biology, 435–55. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_20.
Pełny tekst źródłaColetti, Roberta, i Marta B. Lopes. "Multi-omics Data Integration and Network Inference for Biomarker Discovery in Glioma". W Progress in Artificial Intelligence, 247–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49011-8_20.
Pełny tekst źródłaBalagué, Natàlia, Sergi Garcia-Retortillo, Robert Hristovski i Plamen Ch. Ivanov. "From Exercise Physiology to Network Physiology of Exercise". W Exercise Physiology [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.102756.
Pełny tekst źródłaStreszczenia konferencji na temat "Omic network inference"
Zarayeneh, Neda, Jung Hun Oh, Donghyun Kim, Chunyu Liu, Jean Gao, Sang C. Suh i Mingon Kang. "Integrative Gene Regulatory Network inference using multi-omics data". W 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822711.
Pełny tekst źródłaRaporty organizacyjne na temat "Omic network inference"
Richardson, Ruth. Systems Biology of Dehalococcoides: Using Network Inference Modeling to Integrate Omics Datasets Under Varied Conditions. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2012. http://dx.doi.org/10.21236/ada559471.
Pełny tekst źródła