Relevant bibliographies by topics / Multiomics integration

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multiomics integration'

Author: Grafiati
Published: 21 December 2024
Last updated: 25 July 2025

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Journal articles on the topic "Multiomics integration"

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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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Abubakar, Yaro. "Multiomics integration in anti-tuberculosis drug discovery." Sanamed, no. 00 (2025): 84. https://doi.org/10.5937/sanamed0-51644.

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Despite intensive global efforts, tuberculosis remains one of the leading global health burdens, with antimicrobial resistance being a significant challenge to managing the disease. In addition, the current drugs used to treat tuberculosis suffer from limitations, such as prolonged therapeutic duration and toxicity. Therefore, the development of new anti-tuberculosis drugs is a priority. However, this process faces several challenges. The introduction of a multiomics approach could serve as an ideal platform to accelerate drug development by addressing these challenges. This article reviews th
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Demetci, Pinar, Rebecca Santorella, Björn Sandstede, William Stafford Noble, and Ritambhara Singh. "Single-Cell Multiomics Integration by SCOT." Journal of Computational Biology 29, no. 1 (2022): 19–22. http://dx.doi.org/10.1089/cmb.2021.0477.

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Santiago, Raoul. "Multiomics integration: advancing pediatric cancer immunotherapy." Immuno Oncology Insights 04, no. 07 (2023): 267–72. http://dx.doi.org/10.18609/ioi.2023.038.

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Ma, Dongheng, Canfeng Fan, Tomoya Sano, et al. "Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer." Journal of Personalized Medicine 15, no. 5 (2025): 166. https://doi.org/10.3390/jpm15050166.

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Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC’s heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in
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Valle, Filippo, Matteo Osella, and Michele Caselle. "Multiomics Topic Modeling for Breast Cancer Classification." Cancers 14, no. 5 (2022): 1150. http://dx.doi.org/10.3390/cancers14051150.

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The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of ’omics data. We test this approach on breast cancer samples from the TCGA database, inte
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Boroń, Dariusz, Nikola Zmarzły, Magdalena Wierzbik-Strońska, Joanna Rosińczuk, Paweł Mieszczański, and Beniamin Oskar Grabarek. "Recent Multiomics Approaches in Endometrial Cancer." International Journal of Molecular Sciences 23, no. 3 (2022): 1237. http://dx.doi.org/10.3390/ijms23031237.

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Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of
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Dhieb, Dhoha, and Kholoud Bastaki. "Pharmaco-Multiomics: A New Frontier in Precision Psychiatry." International Journal of Molecular Sciences 26, no. 3 (2025): 1082. https://doi.org/10.3390/ijms26031082.

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The landscape of psychiatric care is poised for transformation through the integration of pharmaco-multiomics, encompassing genomics, proteomics, metabolomics, transcriptomics, epigenomics, and microbiomics. This review discusses how these approaches can revolutionize personalized treatment strategies in psychiatry by providing a nuanced understanding of the molecular bases of psychiatric disorders and individual pharmacotherapy responses. With nearly one billion affected individuals globally, the shortcomings of traditional treatments, characterized by inconsistent efficacy and frequent adver
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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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Ramos, Marcel, Lucas Schiffer, Angela Re, et al. "Software for the Integration of Multiomics Experiments in Bioconductor." Cancer Research 77, no. 21 (2017): e39-e42. http://dx.doi.org/10.1158/0008-5472.can-17-0344.

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Dissertations / Theses on the topic "Multiomics integration"

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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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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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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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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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Book chapters on the topic "Multiomics 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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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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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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Tarazona, Sonia, Leandro Balzano-Nogueira, and Ana Conesa. "Multiomics Data Integration in Time Series Experiments." In Comprehensive Analytical Chemistry. Elsevier, 2018. http://dx.doi.org/10.1016/bs.coac.2018.06.005.

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Conference papers on the topic "Multiomics integration"

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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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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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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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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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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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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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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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Hong, J., L. Medzikovic, W. Sun, et al. "Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension." In American Thoracic Society 2024 International Conference, May 17-22, 2024 - San Diego, CA. American Thoracic Society, 2024. http://dx.doi.org/10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a7249.

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