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Статті в журналах з теми "Multiomics integration"

Автор: Grafiati

Опубліковано: 21 грудня 2024

Оновлено: 25 липня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Cheng, Han, Mengyu Liang, Yiwen Gao, Wenshan Zhao, and Wei-Feng Guo. "Multiomics with Evolutionary Computation to Identify Molecular and Module Biomarkers for Early Diagnosis and Treatment of Complex Disease." Genes 16, no. 3 (2025): 244. https://doi.org/10.3390/genes16030244.

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It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it difficult to meet the high-precision requirements for biomarker characterization in large dimensions. This study reviewed current analysis methods of evolutionary computation (EC) by considering the essential characteristics of DB identification problems and the advantages of EC, aimin

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12

Heck, Ashley, Hiromi Sato, Christine Kang, et al. "Abstract 1880: Advancing spatial discovery multiomics: Integration of a novel 1,000+ plex discovery proteome atlas with an 18,000+ plex whole transcriptome atlas for same-slide investigation of multiple cancer pathologies." Cancer Research 85, no. 8_Supplement_1 (2025): 1880. https://doi.org/10.1158/1538-7445.am2025-1880.

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Abstract Spatial multiomics at high plex represents a transformative approach to understanding complex biological systems. Whereas high plex spatial transcriptomics have transformed tissue analyses, spatial proteomics have been limited by low plex and lacking coverage of major biological pathways. Proteins, which represent the functional units of cellular response and activity, are essential for studying the heterogeneity of cancer and immune pathology. Furthermore, cellular responses to intrinsic and extrinsic stimuli are often driven by post-translational modifications of proteins. Enabling

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13

Hoang Anh, Nguyen, Jung Eun Min, Sun Jo Kim, and Nguyen Phuoc Long. "Biotherapeutic Products, Cellular Factories, and Multiomics Integration in Metabolic Engineering." OMICS: A Journal of Integrative Biology 24, no. 11 (2020): 621–33. http://dx.doi.org/10.1089/omi.2020.0112.

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14

Bosco, Alice, Francesca Arru, Alessandra Abis, Vassilios Fanos, and Angelica Dessì. "Application of Multiomics in Perinatology: A Metabolomics Integration-Focused Review." International Journal of Molecular Sciences 26, no. 9 (2025): 4164. https://doi.org/10.3390/ijms26094164.

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Precision medicine stems from a new approach to the prevention, diagnosis and treatment of patients, due to the shift in focus away from pathology and towards the uniqueness of the individual, personalising the diagnostic–therapeutic pathway. This paradigm shift has been made possible by the emergence of new high-throughput technologies capable of generating large amounts of data on multiple levels of a biological system, identifying pathology-related genes, transcripts, proteins and metabolites. Metabolomics plays a primary role in this context, providing, through non-invasive sampling, a ver

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15

Kashima, Yukie, Yoshitaka Sakamoto, Keiya Kaneko, Masahide Seki, Yutaka Suzuki, and Ayako Suzuki. "Single-cell sequencing techniques from individual to multiomics analyses." Experimental & Molecular Medicine 52, no. 9 (2020): 1419–27. http://dx.doi.org/10.1038/s12276-020-00499-2.

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Abstract Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a

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16

Bisht, Vartika, Katrina Nash, Yuanwei Xu, et al. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (2021): 5763. http://dx.doi.org/10.3390/ijms22115763.

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Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single ce

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17

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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18

Li, Yongmei, Hao Zhuang, Xinran Zhang, et al. "Multiomics Integration Reveals the Landscape of Prometastasis Metabolism in Hepatocellular Carcinoma." Molecular & Cellular Proteomics 17, no. 4 (2018): 607–18. http://dx.doi.org/10.1074/mcp.ra118.000586.

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19

Wang, Xiangdong. "Clinical trans-omics: an integration of clinical phenomes with molecular multiomics." Cell Biology and Toxicology 34, no. 3 (2018): 163–66. http://dx.doi.org/10.1007/s10565-018-9431-3.

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20

Chen, Tyrone, Al J. Abadi, Kim-Anh Lê Cao, and Sonika Tyagi. "multiomics: A user-friendly multi-omics data harmonisation R pipeline." F1000Research 10 (July 6, 2021): 538. http://dx.doi.org/10.12688/f1000research.53453.1.

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Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experi

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21

Chen, Tyrone, Al J. Abadi, Kim-Anh Lê Cao, and Sonika Tyagi. "multiomics: A user-friendly multi-omics data harmonisation R pipeline." F1000Research 10 (August 2, 2023): 538. http://dx.doi.org/10.12688/f1000research.53453.2.

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Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is of immense interest to researchers as it has potential to unlock previously hidden biomolecular relationships leading to early diagnosis, prognosis, and expedited treatments. Many tools for multi-omics data integration are developed. However, these tools are often restricted to highly specific experimental designs, types of omics data, and specific data formats. A major limitation of the field is the lack of a pipeline that can accept dat

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22

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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23

Tesi, Niccolo’, Sven van der Lee, Marc Hulsman, Henne Holstege, and Marcel Reinders. "Bioinformatics Strategies for the Analysis and Integration of Large-Scale Multiomics Data." Journals of Gerontology: Series A 78, no. 4 (2023): 659–62. http://dx.doi.org/10.1093/gerona/glad005.

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24

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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25

Mudadla, Tejaswi, Gayatri Sharma, Apoorva Mishra, and Shefali Gola. "Multifaceted Landscape ofOmics Data." Bio-Algorithms and Med-Systems 20, no. 1 (2024): 22–36. http://dx.doi.org/10.5604/01.3001.0054.8093.

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<b>Objective:</b> This review aims to provide a comprehensive overview of omics fields – including genomics, epigenomics, transcriptomics, proteomics, metabolomics, single- -cell multiomics, microbiomics, and radiomics – and to highlight the significance of integrating these datasets to tackle complex biological questions in systems biology and precision medicine.<b>Methods:</b> The review analyzes current literature across various omics domains, focusing on their individual contributions to cellular functions and their integration challenges. It discusses successful in

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26

Taguchi, Y.-h., and Turki Turki. "Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data." Genes 11, no. 12 (2020): 1493. http://dx.doi.org/10.3390/genes11121493.

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The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized

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27

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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28

Jiang, Yuexu, Duolin Wang, Dong Xu, and Trupti Joshi. "IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation." Methods 173 (February 2020): 16–23. http://dx.doi.org/10.1016/j.ymeth.2019.06.013.

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29

Karalija, Erna, Armin Macanović, and Saida Ibragić. "Revisiting Traditional Medicinal Plants: Integrating Multiomics, In Vitro Culture, and Elicitation to Unlock Bioactive Potential." Plants 14, no. 13 (2025): 2029. https://doi.org/10.3390/plants14132029.

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Traditional medicinal plants are valued for their therapeutic potential, yet the full spectrum of their bioactive compounds often remains underexplored. Recent advances in multiomics technologies, including metabolomics, proteomics, and transcriptomics, combined with in vitro culture systems and elicitor-based strategies, have revolutionized our ability to characterize and enhance the production of valuable secondary metabolites. This review synthesizes current findings on the integration of these approaches to help us understand phytochemical pathways optimising bioactive compound yields. We

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30

Pfau, Thomas, Mafalda Galhardo, Jake Lin, and Thomas Sauter. "IDARE2—Simultaneous Visualisation of Multiomics Data in Cytoscape." Metabolites 11, no. 5 (2021): 300. http://dx.doi.org/10.3390/metabo11050300.

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Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the dat

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31

Yun, Haiyang, Shabana Vohra, David Lara-Astiaso, and Brian J. P. Huntly. "Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy." STAR Protocols 3, no. 4 (2022): 101770. http://dx.doi.org/10.1016/j.xpro.2022.101770.

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32

Li, Xue, Lifeng Yang, and Xiong Jiao. "Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer." Future Oncology, July 25, 2023. http://dx.doi.org/10.2217/fon-2023-0070.

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Aim: To develop a deep learning-based multiomics integration model. Materials & methods: Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model via a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Results: Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863–0.910). Conclusion: The deep learning

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33

Boini, Aishwarya, Vincent Grasso, Heba Taher, and Andrew A. Gumbs. "Artificial intelligence and the impact of multiomics on the reporting of case reports." World Journal of Clinical Cases 13, no. 15 (2025). https://doi.org/10.12998/wjcc.v13.i15.101188.

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The integration of artificial intelligence (AI) and multiomics has transformed clinical and life sciences, enabling precision medicine and redefining disease understanding. Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022, with AI research tripling during this period. Multiomics fields, including genomics and proteomics, also advanced, exemplified by the Human Proteome Project achieving a 90% complete blueprint by 2021. This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting. A review of studies and

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34

Srivastava, Ruby. "Advancing precision oncology with AI-powered genomic analysis." Frontiers in Pharmacology 16 (April 30, 2025). https://doi.org/10.3389/fphar.2025.1591696.

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Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and ident

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35

Neu, Josef, and Christopher J. Stewart. "Neonatal microbiome in the multiomics era: development and its impact on long-term health." Pediatric Research, February 28, 2025. https://doi.org/10.1038/s41390-025-03953-x.

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Abstract The neonatal microbiome has been the focus of considerable research over the past two decades and studies have added fascinating information in terms of early microbial patterns and how these relate to various disease processes. One difficulty with the interpretation of these relationships is that such data is associative and provides little in terms of proof of causality or the underpinning mechanisms. Integrating microbiome data with other omics such as the proteome, inflammatory mediators, and the metabolome is an emerging approach to address this gap. Here we discuss these omics,

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36

Rönn, Tina, Alexander Perfilyev, Nikolay Oskolkov, and Charlotte Ling. "Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-64846-3.

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AbstractType 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors

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37

Flynn, Emily, Ana Almonte-Loya, and Gabriela K. Fragiadakis. "Single-Cell Multiomics." Annual Review of Biomedical Data Science 6, no. 1 (2023). http://dx.doi.org/10.1146/annurev-biodatasci-020422-050645.

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Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The ap

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Hatamikia, Sepideh, Stephanie Nougaret, Camilla Panico, et al. "Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers." European Radiology Experimental 7, no. 1 (2023). http://dx.doi.org/10.1186/s41747-023-00364-7.

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AbstractHigh-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of mult

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Williams, Amanda. "Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont." FEMS Microbiology Ecology, April 23, 2024. http://dx.doi.org/10.1093/femsec/fiae058.

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Abstract Since their radiation in the Middle Triassic period ∼ 240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, a

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40

Jeong, Yunhee, Jonathan Ronen, Wolfgang Kopp, Pavlo Lutsik, and Altuna Akalin. "scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data." BMC Bioinformatics 25, no. 1 (2024). http://dx.doi.org/10.1186/s12859-024-05880-w.

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AbstractThe recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial le

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Wei, Lise, Dipesh Niraula, Evan D. H. Gates, et al. "Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration." British Journal of Radiology, September 3, 2023. http://dx.doi.org/10.1259/bjr.20230211.

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Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Secon

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Wolde, Tesfaye, Vipul Bhardwaj, and Vijay Pandey. "Current Bioinformatics Tools in Precision Oncology." MedComm 6, no. 7 (2025). https://doi.org/10.1002/mco2.70243.

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ABSTRACTIntegrating bioinformatics tools has profoundly transformed precision oncology by identifying essential molecular targets for personalized treatment. The rapid development of high‐throughput sequencing and multiomics technologies creates complex datasets that require robust computational methods to extract meaningful insights. Nonetheless, the clinical application of multiomics data continues to pose significant challenges. This review explores advanced bioinformatics tools utilized within multiomics, emphasizing their pivotal role in discovering cancer biomarkers. Cloud‐based platform

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Kesimoglu, Ziynet Nesibe, and Serdar Bozdag. "SUPREME: multiomics data integration using graph convolutional networks." NAR Genomics and Bioinformatics 5, no. 2 (2023). http://dx.doi.org/10.1093/nargab/lqad063.

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Abstract To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classificati

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Smith, Jennifer R., Marek A. Tutaj, Jyothi Thota, et al. "Standardized pipelines support and facilitate integration of diverse datasets at the Rat Genome Database." Database 2025 (2025). https://doi.org/10.1093/database/baae132.

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Abstract The Rat Genome Database (RGD) is a multispecies knowledgebase which integrates genetic, multiomic, phenotypic, and disease data across 10 mammalian species. To support cross-species, multiomics studies and to enhance and expand on data manually extracted from the biomedical literature by the RGD team of expert curators, RGD imports and integrates data from multiple sources. These include major databases and a substantial number of domain-specific resources, as well as direct submissions by individual researchers. The incorporation of these diverse datatypes is handled by a growing lis

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Chen, Zhuo, Elaheh Pilehvar, Hadi Sadeghi, and Younes Pilehvar. "Precision Reimagined: CRISPR and Multiomics Transform Systemic Lupus Erythematosus Diagnosis and Therapy." International Journal of Rheumatic Diseases 28, no. 4 (2025). https://doi.org/10.1111/1756-185x.70189.

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ABSTRACTSystemic lupus erythematosus (SLE) is a complex autoimmune disorder with diverse clinical manifestations and unpredictable progression, posing significant challenges to accurate diagnosis and effective treatment. Traditional biomarkers and treatments often fail to address the disease's molecular and clinical heterogeneity. Recent advancements in CRISPR gene‐editing technology and multiomics approaches offer transformative opportunities for personalized SLE care by unraveling its underlying molecular complexity and enabling precise therapeutic interventions. CRISPR technology allows tar

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Choudhary, Ratan Kumar, Sunil Kumar B. V., Chandra Sekhar Mukhopadhyay, et al. "Animal Wellness: The Power of Multiomics and Integrative Strategies." Veterinary Medicine International 2024, no. 1 (2024). http://dx.doi.org/10.1155/2024/4125118.

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The livestock industry faces significant challenges, with disease outbreaks being a particularly devastating issue. These diseases can disrupt the food supply chain and the livelihoods of those involved in the sector. To address this, there is a growing need to enhance the health and well‐being of livestock animals, ultimately improving their performance while minimizing their environmental impact. To tackle the considerable challenge posed by disease epidemics, multiomics approaches offer an excellent opportunity for scientists, breeders, and policymakers to gain a comprehensive understanding

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Yu, Ying, Naixin Zhang, Yuanbang Mai, et al. "Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method." Genome Biology 24, no. 1 (2023). http://dx.doi.org/10.1186/s13059-023-03047-z.

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Abstract Background Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. Results As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correc

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Li, Chuan-Xing, Jing Gao, Zicheng Zhang, et al. "Multiomics integration-based molecular characterizations of COVID-19." Briefings in Bioinformatics 23, no. 1 (2021). http://dx.doi.org/10.1093/bib/bbab485.

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Abstract The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation

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Shave, Steven, John C. Dawson, Abdullah M. Athar, Cuong Q. Nguyen, Richard Kasprowicz, and Neil O. Carragher. "Phenonaut; multiomics data integration for phenotypic space exploration." Bioinformatics, March 21, 2023. http://dx.doi.org/10.1093/bioinformatics/btad143.

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Abstract Summary Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high content imaging, proteomics, metabolomics and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation. Availabilit

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Neumann, Elizabeth K. "Spatial Multiomics Toward Understanding Neurological Systems." Journal of Mass Spectrometry 60, no. 6 (2025). https://doi.org/10.1002/jms.5143.

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ABSTRACTDynamic biological processes in the brain involve complex interactions between various cell types, and these interactions span multiple biological scales. Each of these domains are crucial in maintaining brain health. Traditional methods, such as transcriptomics and protein labeling, provide valuable insights but fail to capture the full molecular landscape of neurological function. Multimodal imaging, combining multiple imaging techniques, offers a more comprehensive approach to studying biological systems by integrating different omics technologies. Spatial metabolomics involves usin

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