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Journal articles on the topic 'Multimodal omics'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Lobato-Delgado, Barbara, Torres Blanca María Priego, and Morillo Daniel Sanchez. "Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis." Cancers 14, no. 13 (2022): 3215. https://doi.org/10.3390/cancers14133215.

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Vermeulen, I., T. Dankcer, G. Hoogland, et al. "Multimodal spatial omics in human focal epilepsy." Brain and Spine 2 (2022): 101583. http://dx.doi.org/10.1016/j.bas.2022.101583.

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Eteleeb, Abdallah M., Brenna C. Novotny, Carolina Soriano Tarraga, et al. "Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer’s disease." PLOS Biology 22, no. 4 (2024): e3002607. http://dx.doi.org/10.1371/journal.pbio.3002607.

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Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular

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Xu, Chi, Denghui Liu, Lei Zhang, et al. "AutoOmics: New multimodal approach for multi-omics research." Artificial Intelligence in the Life Sciences 1 (December 2021): 100012. http://dx.doi.org/10.1016/j.ailsci.2021.100012.

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Zhu, Chenxu, Sebastian Preissl, and Bing Ren. "Single-cell multimodal omics: the power of many." Nature Methods 17, no. 1 (2020): 11–14. http://dx.doi.org/10.1038/s41592-019-0691-5.

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Lee, Yoonji, Mingyu Lee, Yoojin Shin, Kyuri Kim, and Taejung Kim. "Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications." International Journal of Molecular Sciences 26, no. 9 (2025): 3949. https://doi.org/10.3390/ijms26093949.

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Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell–cell interactions within their native environments. Despite challenges in data complexity and integrat

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Benkirane, Hakim, Maria Vakalopoulou, David Planchard, et al. "Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology." PLOS Computational Biology 21, no. 6 (2025): e1013012. https://doi.org/10.1371/journal.pcbi.1013012.

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Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor’s microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help understand these interactions. Despite recent advances in representing multimodal data for weakly supervised tasks in the medical domain, achieving a coherent and interpretable fusion of whole slide images and multi-omics data is still a challenge. Each modality operates at distinct biological levels, introducing substantial correlations between and wit

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Maji, Pradipta, and Ankita Mandal. "Multimodal Omics Data Integration Using Max Relevance--Max Significance Criterion." IEEE Transactions on Biomedical Engineering 64, no. 8 (2017): 1841–51. http://dx.doi.org/10.1109/tbme.2016.2624823.

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Yang, Tao, Haohao Li, Yanlei Kang, and Zhong Li. "MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect." Biomolecules 14, no. 8 (2024): 1039. http://dx.doi.org/10.3390/biom14081039.

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Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point clou

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Uzunangelov, Vladislav, Christopher K. Wong, and Joshua M. Stuart. "Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge." PLOS Computational Biology 17, no. 4 (2021): e1008878. http://dx.doi.org/10.1371/journal.pcbi.1008878.

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Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene “signatures”—patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable

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Hayes, C. Nelson, Hikaru Nakahara, Atsushi Ono, Masataka Tsuge, and Shiro Oka. "From Omics to Multi-Omics: A Review of Advantages and Tradeoffs." Genes 15, no. 12 (2024): 1551. http://dx.doi.org/10.3390/genes15121551.

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Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamen

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Wehrle, E. "OMICS-BASED PRECLINICAL MODELS OF MUSCULOSKELETAL REGENERATION." Orthopaedic Proceedings 106-B, SUPP_2 (2024): 55. http://dx.doi.org/10.1302/1358-992x.2024.2.055.

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Despite the major advances in osteosynthesis after trauma, there remains a small proportion of patients (<10%) who exhibit delayed healing and/or eventual progression to non-union. While known risk factors exist, e.g. advanced age or diabetes, the exact molecular mechanism underlying the impaired healing is largely unknown and identifying which specific patient will develop healing complications is still not possible in clinical practice. The talk will cover our novel multimodal approaches in small animals, which have the potential to precisely capture and understand biological changes duri

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Murai, Toshiyuki, and Satoru Matsuda. "Integrated Multimodal Omics and Dietary Approaches for the Management of Neurodegeneration." Epigenomes 7, no. 3 (2023): 20. http://dx.doi.org/10.3390/epigenomes7030020.

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Neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease, are caused by a combination of multiple events that damage neuronal function. A well-characterized biomarker of neurodegeneration is the accumulation of proteinaceous aggregates in the brain. However, the gradually worsening symptoms of neurodegenerative diseases are unlikely to be solely due to the result of a mutation in a single gene, but rather a multi-step process involving epigenetic changes. Recently, it has been suggested that a fraction of epigenetic alternations may be correlated to neurodegeneration in

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Fujita, Suguru, Yasuaki Karasawa, Ken-ichi Hironaka, Y. h. Taguchi, and Shinya Kuroda. "Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome." PLOS ONE 18, no. 2 (2023): e0281594. http://dx.doi.org/10.1371/journal.pone.0281594.

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High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion o

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Mandal, Ankita, and Pradipta Maji. "FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data." IEEE Transactions on Cybernetics 48, no. 4 (2018): 1229–41. http://dx.doi.org/10.1109/tcyb.2017.2685625.

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Dong, Yixing, and Raphael Gottardo. "An approach for integrating multimodal omics data into sparse and interpretable models." Cell Reports Methods 4, no. 2 (2024): 100718. http://dx.doi.org/10.1016/j.crmeth.2024.100718.

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Davitavyan, Suren, Gevorg Martirosyan, Gohar Mkrtchyan, et al. "Integrated analysis of -omic landscapes in breast cancer subtypes." F1000Research 13 (June 3, 2024): 564. http://dx.doi.org/10.12688/f1000research.148778.1.

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The subtypes of breast cancer exhibit diverse histology, molecular features, therapeutic response, aggressiveness, and patient outcomes. Multi-omics high-throughput technologies, which are widely used in cancer research, generated waste amounts of multimodal omic datasets calling for new approaches of integrated analyses to uncover patterns of transcriptomic, genomic, and epigenetic changes in breast cancer subtypes and connect them to disease clinical characteristics. Here, we applied multi-layer self-organizing map (ml-SOM) algorithms to PAM50-classified TCGA breast cancer samples to disenta

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Li, Wei, Binchun Liu, Weiqian Wang, et al. "Lung Cancer Stage Prediction Using Multi-Omics Data." Computational and Mathematical Methods in Medicine 2022 (July 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/2279044.

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Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by in

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Isavand, Pouria, Sara Sadat Aghamiri, and Rada Amin. "Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells." Biomedicines 12, no. 8 (2024): 1753. http://dx.doi.org/10.3390/biomedicines12081753.

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Given advancements in large-scale data and AI, integrating multimodal artificial intelligence into cancer research can enhance our understanding of tumor behavior by simultaneously processing diverse biomedical data types. In this review, we explore the potential of multimodal AI in comprehending B-cell non-Hodgkin lymphomas (B-NHLs). B-cell non-Hodgkin lymphomas (B-NHLs) represent a particular challenge in oncology due to tumor heterogeneity and the intricate ecosystem in which tumors develop. These complexities complicate diagnosis, prognosis, and therapy response, emphasizing the need to us

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Zhang, R., X. Shen, L. Huang, S. T. Feng, R. Mao, and X. Li. "P0553 MRI neurophenotype reflecting brain-gut interactions to predict intestinal disease progression in patients with Crohn’s disease." Journal of Crohn's and Colitis 19, Supplement_1 (2025): i1119. https://doi.org/10.1093/ecco-jcc/jjae190.0727.

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Abstract Background There is considerable recent interest in the role of brain-gut axis in the pathogenesis and manifestations of Crohn’s disease (CD). We developed a multimodal neuroimaging-based model to characterize the neurophenotype of CD patients and predict intestinal disease progression, using multi-omics data to demonstrate its validity. Methods This prospective study enrolled 109 CD patients who underwent baseline tests (including multimodal neuroimaging, psychological scales, MR enterography, ileocolonoscopy) and fecal/blood samples collection within one week. The neurophenotype of

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Yang, Zi-Yi, Liang-Yong Xia, Hui Zhang, and Yong Liang. "MSPL: Multimodal Self-Paced Learning for Multi-Omics Feature Selection and Data Integration." IEEE Access 7 (2019): 170513–24. http://dx.doi.org/10.1109/access.2019.2955958.

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22

Gurke, Robert, Annika Bendes, John Bowes, et al. "Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis." Biomedicines 10, no. 10 (2022): 2387. http://dx.doi.org/10.3390/biomedicines10102387.

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The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and

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Disselhorst, Jonathan A., Marcel A. Krueger, S. M. Minhaz Ud-Dean, et al. "Linking imaging to omics utilizing image-guided tissue extraction." Proceedings of the National Academy of Sciences 115, no. 13 (2018): E2980—E2987. http://dx.doi.org/10.1073/pnas.1718304115.

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Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characteriz

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Danila, Bredikhin, Kats Ilia, and Stegle Oliver. "Muon: multimodal omics analysis framework." October 8, 2021. https://doi.org/10.5281/zenodo.5776349.

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Muon is a multimodal omics analysis framework. Muon is built around MuData — an open standard for multimodal data. Here, a snapshot of the source code for mudata and muon Python packages is provided together with tutorials (muon-tutorials) and libraries for other languages (MuDataMAE and MuDataSeurat for R, Muon.jl for Julia). Up-to-date source code is made available on GitHub.

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Li, Bingjun, and Sheida Nabavi. "A multimodal graph neural network framework for cancer molecular subtype classification." BMC Bioinformatics 25, no. 1 (2024). http://dx.doi.org/10.1186/s12859-023-05622-4.

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Abstract Background The recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Most current multi-omics integrative models use either an early fusion in the form of concatenation or late fusion with a separate feature extractor for each omic, which are mainly based on deep neural networks. Due to the nature of biologica

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Meng, Dian, Yu Feng, Kaishen Yuan, et al. "scMMAE: masked cross-attention network for single-cell multimodal omics fusion to enhance unimodal omics." Briefings in Bioinformatics 26, no. 1 (2024). https://doi.org/10.1093/bib/bbaf010.

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Abstract Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type. Moreover, as the majority of single-cell cohorts only encompass one omics, it becomes critical to transfer the knowledge learnt from multimodal omics to enhance unimodal omics a

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Lim, Jongsu, Chanho Park, Minjae Kim, Hyukhee Kim, Junil Kim, and Dong-Sung Lee. "Advances in single-cell omics and multiomics for high-resolution molecular profiling." Experimental & Molecular Medicine, March 5, 2024. http://dx.doi.org/10.1038/s12276-024-01186-2.

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AbstractSingle-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, th

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Liu, Chunlei, Hao Huang, and Pengyi Yang. "Multi-task learning from multimodal single-cell omics with Matilda." Nucleic Acids Research, March 13, 2023. http://dx.doi.org/10.1093/nar/gkad157.

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Abstract Multimodal single-cell omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of multimodal single-cell omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of multimodal single-cell omics data. By leveraging the interrelationship amon

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Bredikhin, Danila, Ilia Kats, and Oliver Stegle. "MUON: multimodal omics analysis framework." Genome Biology 23, no. 1 (2022). http://dx.doi.org/10.1186/s13059-021-02577-8.

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AbstractAdvances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. Here, we present a data standard and an analysis framework for multi-omics, MUON, designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible and interoperable data structure. MUON enables a versatile range of analy

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Ellis, Dorothy, Arkaprava Roy, and Susmita Datta. "Clustering single-cell multimodal omics data with jrSiCKLSNMF." Frontiers in Genetics 14 (June 9, 2023). http://dx.doi.org/10.3389/fgene.2023.1179439.

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Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise.Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-L

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Mataraso, Samson J., Camilo A. Espinosa, David Seong, et al. "A machine learning approach to leveraging electronic health records for enhanced omics analysis." Nature Machine Intelligence, January 16, 2025. https://doi.org/10.1038/s42256-024-00974-9.

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Abstract Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electroni

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Zhou, Yuan, Pei Geng, Shan Zhang, et al. "Multimodal functional deep learning for multiomics data." Briefings in Bioinformatics 25, no. 5 (2024). http://dx.doi.org/10.1093/bib/bbae448.

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Abstract With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics

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Li, Yunjin, Lu Ma, Duojiao Wu, and Geng Chen. "Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine." Briefings in Bioinformatics, March 27, 2021. http://dx.doi.org/10.1093/bib/bbab024.

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Abstract Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summari

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Zhang, Chengming, Yiwen Yang, Shijie Tang, Kazuyuki Aihara, Chuanchao Zhang, and Luonan Chen. "Contrastively generative self-expression model for single-cell and spatial multimodal data." Briefings in Bioinformatics, July 28, 2023. http://dx.doi.org/10.1093/bib/bbad265.

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Abstract Advances in single-cell multi-omics technology provide an unprecedented opportunity to fully understand cellular heterogeneity. However, integrating omics data from multiple modalities is challenging due to the individual characteristics of each measurement. Here, to solve such a problem, we propose a contrastive and generative deep self-expression model, called single-cell multimodal self-expressive integration (scMSI), which integrates the heterogeneous multimodal data into a unified manifold space. Specifically, scMSI first learns each omics-specific latent representation and self-

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Zuo, Chunman, Junchao Zhu, Jiawei Zou, and Luonan Chen. "Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data." Clinical and Translational Medicine 15, no. 5 (2025). https://doi.org/10.1002/ctm2.70331.

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AbstractAnalysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi‐omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo‐relations, as well as inference of intra‐ and inter‐cellular molecular networks that drive disease progression. We also discuss strategies to

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Bai, Dongsheng, and Chenxu Zhu. "Single-cell technologies for multimodal omics measurements." Frontiers in Systems Biology 3 (April 21, 2023). http://dx.doi.org/10.3389/fsysb.2023.1155990.

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The recent surge in single-cell genomics, including the development of a wide range of experimental and computational approaches, has provided insights into the complex molecular networks of cells during development and in human diseases at unprecedented resolution. Single-cell transcriptome analysis has enabled high-resolution investigation of cellular heterogeneity in a wide range of cell populations ranging from early embryos to complex tissues—while posing the risk of only capturing a partial picture of the cells’ complex molecular networks. Single-cell multiomics technologies aim to bridg

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Tabakhi, Sina, Mohammod Naimul Islam Suvon, Pegah Ahadian, and Haiping Lu. "Multimodal Learning for Multi-Omics: A Survey." World Scientific Annual Review of Artificial Intelligence, December 16, 2022. http://dx.doi.org/10.1142/s2811032322500047.

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Yu, Lijia, Chunlei Liu, Jean Yee Hwa Yang, and Pengyi Yang. "Ensemble deep learning of embeddings for clustering multimodal single-cell omics data." Bioinformatics, June 14, 2023. http://dx.doi.org/10.1093/bioinformatics/btad382.

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Abstract Motivation Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterisation of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. Results We propose SnapCCESS for clusteri

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Ang, Guo, Chen Zhiyu, Ma Yinzhong, et al. "Multimodal Coregistration and Fusion between Spatial Metabol-omics and Biomedical Imaging." March 6, 2023. https://doi.org/10.5281/zenodo.7700528.

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Spatial omics is being increasingly "fused" with classical biomedical imaging modalities to complement them with spatially-resolved molecular profiling. Before the multimodal information can be fused, all contributing datasets must be spatially aligned through a process called coregistration. Here we present a multimodal coregistration framework with great genericity and accuracy for spatial omics studies. An original dimension reduction algorithm "PseudoRep" is used to bridge cross-modal gaps. Then, a Deep Learning-based transformation algorithm "DeepReg" is adop

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Fan, Ziling, Zhangqi Jiang, Hengyu Liang, and Chao Han. "Pancancer survival prediction using a deep learning architecture with multimodal representation and integration." Bioinformatics Advances, January 23, 2023. http://dx.doi.org/10.1093/bioadv/vbad006.

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Abstract Motivation Use of multi-omics data carrying comprehensive signals about the disease is strongly desirable for understanding and predicting disease progression, cancer particularly as a serious disease with a high mortality rate. However, recent methods currently fail to effectively utilize the multi-omics data for cancer survival prediction and thus significantly limiting the accuracy of survival prediction using omics data. Results In this work, we constructed a deep learning model with multimodal representation and integration to predict the survival of patients using multi-omics da

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Xu, Jing, De‐Shuang Huang, and Xiujun Zhang. "scmFormer Integrates Large‐Scale Single‐Cell Proteomics and Transcriptomics Data by Multi‐Task Transformer." Advanced Science, March 14, 2024. http://dx.doi.org/10.1002/advs.202307835.

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AbstractTransformer‐based models have revolutionized single cell RNA‐seq (scRNA‐seq) data analysis. However, their applicability is challenged by the complexity and scale of single‐cell multi‐omics data. Here a novel single‐cell multi‐modal/multi‐task transformer (scmFormer) is proposed to fill up the existing blank of integrating single‐cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large‐scale single‐cell multimodal data and heterogeneous multi‐batch paired multi‐omics data, while preserving shared information a

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Luo, Bingying, Fei Teng, Guo Tang, et al. "StereoMM: a graph fusion model for integrating spatial transcriptomic data and pathological images." Briefings in Bioinformatics 26, no. 3 (2025). https://doi.org/10.1093/bib/bbaf210.

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Abstract Spatial omics technologies, generating high-throughput and multimodal data, have necessitated the development of advanced data integration methods to facilitate comprehensive biological and clinical treatment discoveries. Based on the cross-attention concept, we developed an AI learning based toolchain called StereoMM, a graph based fusion model that can incorporate omics data such as gene expression, histological images, and spatial location. StereoMM uses an attention module for omics data interaction and a graph autoencoder to integrate spatial positions and omics data in a self-su

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Liu, Yufang, Yongkai Chen, Haoran Lu, Wenxuan Zhong, Guo-Cheng Yuan, and Ping Ma. "Orthogonal multimodality integration and clustering in single-cell data." BMC Bioinformatics 25, no. 1 (2024). http://dx.doi.org/10.1186/s12859-024-05773-y.

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AbstractMultimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sourc

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Ogris, Christoph, Yue Hu, Janine Arloth, and Nikola S. Müller. "Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-85544-4.

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AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach,

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Lin, Xiang, Tian Tian, Zhi Wei, and Hakon Hakonarson. "Clustering of single-cell multi-omics data with a multimodal deep learning method." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-35031-9.

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AbstractSingle-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for si

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Marconato, Luca, Giovanni Palla, Kevin A. Yamauchi, et al. "SpatialData: an open and universal data framework for spatial omics." Nature Methods, March 20, 2024. http://dx.doi.org/10.1038/s41592-024-02212-x.

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AbstractSpatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation

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Yao, Minhao, and Zhonghua Liu. "An Introduction to Causal Inference Methods with Multi‐omics Data." Current Protocols 5, no. 6 (2025). https://doi.org/10.1002/cpz1.70168.

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AbstractOmics biomarkers play a pivotal role in personalized medicine by providing molecular‐level insights into the etiology of diseases, guiding precise diagnostics, and facilitating targeted therapeutic interventions. Recent advancements in omics technologies have resulted in an increasing abundance of multimodal omics data, providing unprecedented opportunities for identifying novel omics biomarkers for human diseases. Mendelian randomization (MR) is a practically useful causal inference method that uses genetic variants as instrumental variables to infer causal relationships between omics

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Itai, Yonatan, Nimrod Rappoport, and Ron Shamir. "Integration of gene expression and DNA methylation data across different experiments." Nucleic Acids Research, July 3, 2023. http://dx.doi.org/10.1093/nar/gkad566.

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Abstract Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the tw

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Park, Jiwoon, Junbum Kim, Tyler Lewy, et al. "Spatial omics technologies at multimodal and single cell/subcellular level." Genome Biology 23, no. 1 (2022). http://dx.doi.org/10.1186/s13059-022-02824-6.

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AbstractSpatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of sing

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Olsen, Christian. "Why Multimodal Data is Growing In Pharma." Onco Zine - The International Oncology Network, July 29, 2024. http://dx.doi.org/10.14229/onco.2024.07.29.001.

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Technology advances in both R&D and patient care have created a deluge of data, including instrument and experimental data, -omics data, clinical study results, patient data, patent data, publication data, etc. Making connections between these different datasets is essential to developing impactful medicines and driving more personalized patient care.

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