Gotowe bibliografie tematyczne / Multiomic integration

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Gotowa bibliografia na temat „Multiomic integration”

Autor: Grafiati
Data publikacji: 16 listopada 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Multiomic integration"

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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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Blutt, Sarah E., Cristian Coarfa, Josef Neu, and Mohan Pammi. "Multiomic Investigations into Lung Health and Disease." Microorganisms 11, no. 8 (2023): 2116. http://dx.doi.org/10.3390/microorganisms11082116.

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Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics inv
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Ramos, Marcel, Ludwig Geistlinger, Sehyun Oh, et al. "Multiomic Integration of Public Oncology Databases in Bioconductor." JCO Clinical Cancer Informatics, no. 4 (October 2020): 958–71. http://dx.doi.org/10.1200/cci.19.00119.

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PURPOSE Investigations of the molecular basis for the development, progression, and treatment of cancer increasingly use complementary genomic assays to gather multiomic data, but management and analysis of such data remain complex. The cBioPortal for cancer genomics currently provides multiomic data from > 260 public studies, including The Cancer Genome Atlas (TCGA) data sets, but integration of different data types remains challenging and error prone for computational methods and tools using these resources. Recent advances in data infrastructure within the Bioconductor project enable a n
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Hatami, Elham, Hye-Won Song, Hongduan Huang, et al. "Integration of single-cell transcriptomic and chromatin accessibility on heterogenicity of human peripheral blood mononuclear cells utilizing microwell-based single-cell partitioning technology." Journal of Immunology 212, no. 1_Supplement (2024): 1508_5137. http://dx.doi.org/10.4049/jimmunol.212.supp.1508.5137.

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Abstract Single-cell RNA sequencing (scRNA-Seq) deepens our understanding of cellular development and heterogeneity. However, limitations exist in unraveling cell states and gene regulatory programs. Chromatin state profiles assess gene expression potential and offer insights into transcriptional regulation. Integrated with gene expression data, chromatin accessibility region (CAR) profiles establish fundamental gene regulatory logic for cell fate. ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing) is a highly potent approach for profiling genome-wide CARs. To investigate t
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Antequera-González, Borja, Neus Martínez-Micaelo, Carlos Sureda-Barbosa, et al. "Specific Multiomic Profiling in Aortic Stenosis in Bicuspid Aortic Valve Disease." Biomedicines 12, no. 2 (2024): 380. http://dx.doi.org/10.3390/biomedicines12020380.

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Introduction and purpose: Bicuspid aortic valve (BAV) disease is associated with faster aortic valve degeneration and a high incidence of aortic stenosis (AS). In this study, we aimed to identify differences in the pathophysiology of AS between BAV and tricuspid aortic valve (TAV) patients in a multiomics study integrating metabolomics and transcriptomics as well as clinical data. Methods: Eighteen patients underwent aortic valve replacement due to severe aortic stenosis: 8 of them had a TAV, while 10 of them had a BAV. RNA sequencing (RNA-seq) and proton nuclear magnetic resonance spectroscop
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Silberberg, Gilad, Clare Killick-Cole, Yaron Mosesson, et al. "Abstract 854: A pharmaco-pheno-multiomic integration analysis of pancreatic cancer: A highly predictive biomarker model of biomarkers of Gemcitabine/Abraxane sensitivity and resistance." Cancer Research 83, no. 7_Supplement (2023): 854. http://dx.doi.org/10.1158/1538-7445.am2023-854.

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Abstract The overall survival of patients diagnosed with Pancreatic Cancer remains low. Initial responses to current therapeutic interventions are below 50%, leading to a high mortality rate shortly after diagnosis. To date, only a companion diagnostic, non-specific for pancreatic cancer, has been approved for this indication. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcome and improved patient stratification. In this study, we performed an exhaustive analysis to identify predictive biomarke
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Reem, El Kabbout, Abi Sleimen Antonella, Boucherat Olivier, Bonnet Sebastien, Provencher Steeve, and Potus Francois. "Multiomics Integration for Identifying Treatment Targets, Drug Development, and Diagnostic Designs in PAH." Advances in Pulmonary Hypertension 23, no. 2 (2025): 33–42. https://doi.org/10.21693/1933-088x-23.2.33.

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Unraveling the complexities of pulmonary arterial hypertension (PAH) is challenging due to its multifaceted nature, encompassing molecular, cellular, tissue, and organ-level alterations. The advent of omics technologies, including genomics, ­epigenomics, transcriptomics, metabolomics, and proteomics, has generated a vast array of public and nonpublic datasets from both humans and model organisms, opening new avenues for understanding PAH. However, the insights provided by individual omics datasets into the molecular mechanisms of PAH are inherently limited. In response, efforts are increasing
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Culley, Christopher, Supreeta Vijayakumar, Guido Zampieri, and Claudio Angione. "A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth." Proceedings of the National Academy of Sciences 117, no. 31 (2020): 18869–79. http://dx.doi.org/10.1073/pnas.2002959117.

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Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic fl
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Pratapa, Aditya, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, and Arutha Kulasinghe. "Abstract 5503: Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response." Cancer Research 84, no. 6_Supplement (2024): 5503. http://dx.doi.org/10.1158/1538-7445.am2024-5503.

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Abstract Background Targeted immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 therapy offer durable treatment of mucosal head and neck squamous cell cancer (HNSCC), in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. However, currently available biomarker signatures for targeted ICI therapies have limited predictive value. Our recent ultrahigh-plex profiling of HNSCC tissue with 100+ cancer hallmarks of tumor and immunobiology uncovered distinct spatial domains that serve as defining factors for clinical response and resistance. Methods Our unbiased analysis of wh
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Jamal, Sabri, Michael J. Wilson, Jean Teyssandier, et al. "Abstract 6296: Unlocking scalable and efficient multiomic analysis of 5- and 6-base genomes." Cancer Research 85, no. 8_Supplement_1 (2025): 6296. https://doi.org/10.1158/1538-7445.am2025-6296.

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We present a computational toolkit to analyze 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) Cytosine modifications at scale and describe its performance on a liquid biopsy dataset. Methylation data has diverse applications in cancer, including early-stage diagnosis through liquid biopsy, classification to guide treatment pathways, and prognosis. However, analyzing methylation data poses significant challenges. Many existing tools are difficult to use and struggle to scale as sample sizes grow. This lack of scalability makes standard tasks, such as identifying differentially methyla
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Rozprawy doktorskie na temat "Multiomic integration"

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Bretones, Santamarina Jorge. "Integrated multiomic analysis, synthetic lethality inference and network pharmacology to identify SWI/SNF subunit-specific pathway alterations and targetable vulnerabilities." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL049.

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De nos jours, la communauté scientifique s'accorde sur la nécessité de diagnostics et de thérapies personnalisés pour les patients atteints de cancer, conçus par des études translationnelles combinant approches expérimentales et statistiques. Les défis actuels incluent la validation de modèles expérimentaux précliniques et leur profilage multi-omiques, ainsi que la conception de méthodes bioinformatiques et mathématiques dédiées pour identifier les combinaisons de médicaments optimales pour chaque patient.Cette thèse a visé à concevoir de telles approches statistiques pour analyser différents
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Coronado, Zamora Marta. "Mapping natural selection through the drosophila melanogaster development following a multiomics data integration approach." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/666761.

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La teoria de l'evolució de Charles Darwin proposa que les adaptacions dels organismes sorgeixen com a conseqüència del procés de la selecció natural. La selecció natural deixa una empremta característica en els patrons de variació genètica que pot detectar-se mitjançant mètodes estadístics d'anàlisi genòmica. Avui en dia podem inferir l'acció de la selecció natural en el genoma i fins i tot quantificar quina proporció de les noves variants genètiques que incorpora una espècie són adaptatives. L’era genòmica ha conduït a la situació paradoxal en la qual disposem de més informació sobre la selec
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Bodily, Weston Reed. "Integrative Analysis to Evaluate Similarity Between BRCAness Tumors and BRCA Tumors." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6800.

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The term "BRCAness" is used to describe breast-cancer patients who lack a germline mutation in BRCA1 or BRCA2, yet who are believed to express characteristics similar to patients who do have a germline mutation in BRCA1 or BRCA2. Although it is hypothesized that BRCAness is related to deficiency in the homologous recombination repair (HRR) pathways, relatively little is understood about what drives BRCAness or what criteria should be used to assign patients to this category. We hypothesized that patients whose tumor carries a genomic or epigenomic aberration in BRCA1 or BRCA2 should be classif
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Iperi, Cristian. "Identification of B lymphocyte alterations in systemic lupus erythematosus and Sjögren syndrome using multiomics integration approach." Electronic Thesis or Diss., Brest, 2024. http://theses-scd.univ-brest.fr/2024/These-2024-SVS-Immunologie-IPERI_Cristian.pdf.

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Le métabolisme joue un rôle crucial dans l'orchestration et la régulation des processus immunologiques dans les cellules immunitaires, y compris les lymphocytes B.Cette branche, appelée immunométabolisme, étudie comment les altérations métaboliques influencent les réponses immunitaires et le développement de pathologies auto-immunes.Ce manuscrit traite spécifiquement du lupus érythémateux systémique (LES) et du syndrome de Sjögren (SjS) via une approche axée sur leurs altérations métaboliques dans les cellules B et leur environnement. L'intérêt pour ces maladies réside dans les mécanismes bien
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Części książek na temat "Multiomic integration"

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Lee, Jae Jin, Philip Sell, and Hyungjin Eoh. "Multiomics Integration of Tuberculosis Pathogenesis." In Integrated Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15955-8_45.

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HajYasien, Ahmed. "Introduction to Multiomics Technology." In Machine Learning Methods for Multi-Omics Data Integration. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_1.

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Liu, Qian, Shujun Huang, Zhongyuan Zhang, Ted M. Lakowski, Wei Xu, and Pingzhao Hu. "Multiomics-Based Tensor Decomposition for Characterizing Breast Cancer Heterogeneity." In Machine Learning Methods for Multi-Omics Data Integration. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_8.

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Kamar, Mohd Danish, Madhu Bala, Gaurav Prajapati, and Ratan Singh Ray. "Multiomics Data Integration in Understanding of Inflammation and Inflammatory Diseases." In Inflammation Resolution and Chronic Diseases. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0157-5_11.

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Ning, Kang, and Yuxue Li. "Synthetic Biology-Related Multiomics Data Integration and Data Mining Techniques." In Synthetic Biology and iGEM: Techniques, Development and Safety Concerns. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2460-8_3.

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Islam, Mousona. "Strategic Short Note: Integration of Multiomics Approaches for Sustainable Crop Improvement." In IoT and AI in Agriculture. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1263-2_9.

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Zhang, Tianyu, Liwei Zhang, Philip R. O. Payne, and Fuhai Li. "Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models." In Methods in Molecular Biology. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0849-4_12.

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Sekar, Aishwarya, and Gunasekaran Krishnasamy. "Integrating Machine Learning Strategies with Multiomics to Augment Prognosis of Chronic Diseases." In Bioinformatics and Computational Biology. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003331247-9.

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Lee, Hayan, Gilbert Feng, Ed Esplin, and Michael Snyder. "Predictive Signatures for Lung Adenocarcinoma Prognostic Trajectory by Multiomics Data Integration and Ensemble Learning." In Mathematical and Computational Oncology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91241-3_2.

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Mukhopadhyay, Sohini, Nivedhitha Ulaganathan, Prashanth Dumpuri, and Palok Aich. "Integrative AI-Based Approaches to Connect the Multiome to Use Microbiome-Metabolome Interactive Outcome as Precision Medicine." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4690-8_2.

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Streszczenia konferencji na temat "Multiomic integration"

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Bhattacharyya, Rupam, Nicholas Henderson, and Veerabhadran Baladandayuthapani. "BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration." In Pacific Symposium on Biocomputing 2023. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811270611_0026.

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Singhal, Pankhuri, Shefali S. Verma, Scott M. Dudek, and Marylyn D. Ritchie. "Neural network-based multiomics data integration in Alzheimer's disease." In GECCO '19: Genetic and Evolutionary Computation Conference. ACM, 2019. http://dx.doi.org/10.1145/3319619.3321920.

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Bhat, Aadil Rashid, and Rana Hashmy. "Artificial Intelligence-based Multiomics Integration Model for Cancer Subtyping." In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2022. http://dx.doi.org/10.23919/indiacom54597.2022.9763283.

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Jiang, Yuexu, Yanchun Liang, Duolin Wang, Dong Xu, and Trupti Joshi. "IMPRes: Integrative MultiOmics pathway resolution algorithm and tool." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8218016.

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Wheelock, Åsa M. "Multiomics integration-based molecular characterizations in COPD and post-COVID." In RExPO23. REPO4EU, 2023. http://dx.doi.org/10.58647/rexpo.23033.

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Jagtap, Surabhi, Abdulkadir Celikkanat, Aurelic Piravre, Frederiuue Bidard, Laurent Duval, and Fragkiskos D. Malliaros. "Multiomics Data Integration for Gene Regulatory Network Inference with Exponential Family Embeddings." In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616279.

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Koca, Mehmet Burak, and Fatih Erdoğan Sevilgen. "Comparative Analysis of Fusion Techniques for Integrating Single-cell Multiomics Datasets." In 2024 32nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2024. http://dx.doi.org/10.1109/siu61531.2024.10601063.

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Singh, Satishkumar, Fouad Choueiry, Amber Hart, Anuvrat Sircar, Jiangjiang Zhu, and Lalit Sehgal. "Abstract 2351: Multiomics integration elucidates onco-metabolic modulators of drug resistance in lymphoma." 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.am2021-2351.

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Bareche, Yacine, David Venet, Philippe Aftimos, et al. "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.

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Alkhateeb, Abedalrhman, Li Zhou, Ashraf Abou Tabl, and Luis Rueda. "Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2020. http://dx.doi.org/10.1145/3388440.3415992.

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