Integrated omics: tools, advances and future approaches
Abstract
With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. The ultimate goal is toward the holistic realization of a 'systems biology' understanding of the biological question. Commonly used approaches are currently limited by the 3 i's - integration, interpretation and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.
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Data integration is one of the primary barriers to effective multi-omics research and reaching the desired holistic view of the biological system being studied.
“However, data integration is one of the primary barriers to effective multi-omics research and reaching the desired holistic view of the biological system being studied (Misra et al., 2019).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Misra et al., 2019
Most multi-omics datasets have the curse of dimensionality, with far fewer samples than features.
“Most multi-omics datasets suffer from the classical ‗curse of dimensionality' problem, i.e. having much fewer observation samples (n) than multi-omics features (p) (Misra et al., 2019).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Misra et al., 2019
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Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723 · see citing works
“[92] recently reviewed different tools available for multi-omics data integration in detail.”
MethodIntegration strategies of multi-omics data for machine learning analysis
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.030 · Cited by 696 · see citing works
“The advent of powerful and inexpensive screening technologies [1] recently produced huge amounts of biological data that opened the way to a new era of therapeutics and personalized medicine [2].”
BackgroundApplications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545 · see citing works
The Need for Multi-Omics Biomarker Signatures in Precision Medicine
Olivier, M., Asmis, R., Hawkins, G. A., Howard, T. D., & Cox, L. A. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20194781 · Cited by 504 · see citing works
“Additional tools will be needed to perform these complex integrated analyses using strict statistical models, and to help in the interpretation and biological annotation of the obtained co-expression networks or clusters of multi-omics analytes [77].”
MethodMulti-Omics Profiling for Health
Babu, M., & Snyder, M. P. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100561 · Cited by 419 · see citing works
“2) (144 – 146).”
Menyhárt, O., & Győrffy, B. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.009 · Cited by 388 · see citing works
“Excellent comprehensive descriptions from several perspectives are available elsewhere [6–11], discussing data resources [6], multi-omics fusion methods ideal for matched samples [7], comparing supervised, semi-supervised, and unsupervised integrative approaches [8], examining the development of standardized analytica…”
BackgroundMachine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380 · see citing works
“The problem of missing data is exacerbated in multi-omics studies as there can be more samples with missing values [108].”
BackgroundFecal microbiota transplantation: current challenges and future landscapes
Yadegar, A., Bar‐Yoseph, H., Monaghan, T. M., Pakpour, S., Severino, A., Kuijper, E. J., Smits, W. K., Terveer, E. M., Neupane, S., Nabavi-Rad, A., et al. (2024) · Clinical Microbiology Reviews · doi:10.1128/cmr.00060-22 · Cited by 379 · see citing works
State of the field in multi-omics research: from computational needs to data mining and sharing
Krassowski, M., Das, V., Sahu, S. K., & Misra, B. B. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.610798 · Cited by 351 · see citing works
“Firstly, each individual omics analysis presents a multitude of challenges (Gomez-Cabrero et al., 2014; Misra et al., 2019).”
BackgroundRecent advances on ecological effects of microplastics on soil environment
Ya, H., Jiang, B., Xing, Y., Zhang, T., Lv, M., & Wang, X. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149338 · Cited by 350 · see citing works
Multi-omics integration in biomedical research – A metabolomics-centric review
Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2020) · Analytica Chimica Acta · doi:10.1016/j.aca.2020.10.038 · Cited by 359 · see citing works
“The importance of study design and temporal ordering of sample collection [35,44,51,52], as well as guidelines for appropriate data preprocessing [30,51], have been discussed in previous reviews and are beyond the scope of this review.”
BackgroundMethodInfluentialBiomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality
García-Gutiérrez, M. S., Navarrete, F., Sala, F., Gasparyan, A., Austrich-Olivares, A., & Manzanares, J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00432 · Cited by 331 · see citing works
“One of the major challenges still lying ahead is the way to integrate the plethora of data obtained from each ‘omics’ to reach the holistic realization of a ‘systems biology’ understanding the biological question (125).”
BackgroundMishra, S., Lin, Z., Pang, S., Zhang, W., Bhatt, P., & Chen, S. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.632059 · Cited by 333 · see citing works
Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare
Mohr, A. E., Ortega-Santos, C. P., Whisner, C. M., Klein-Seetharaman, J., & Jasbi, P. (2024) · Biomedicines · doi:10.3390/biomedicines12071496 · Cited by 311 · see citing works
“This issue arises when datasets possess a disproportionately large number of features ( f ) compared to available samples ( n ) [97].”
ResultMetabolomics as an Emerging Tool for the Study of Plant–Pathogen Interactions
Castro-Moretti, F. R., Gentzel, I. N., Mackey, D. M., & Alonso, A. P. (2020) · Metabolites · doi:10.3390/metabo10020052 · Cited by 275 · see citing works
Sanches, P. H. G., de Melo, N. C., Porcari, A. M., & de Carvalho, L. M. (2024) · Biology · doi:10.3390/biology13110848 · Cited by 269 · see citing works
Artificial intelligence aids in development of nanomedicines for cancer management
Tan, P., Chen, X., Zhang, H., Wei, Q., & Luo, K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.01.005 · Cited by 254 · see citing works
Multi-omics data integration considerations and study design for biological systems and disease
Graw, S., Chappell, K., Washam, C. L., Gies, A. J., Bird, J. T., Robeson, M. S., & Byrum, S. D. (2020) · Molecular Omics · doi:10.1039/d0mo00041h · Cited by 223 · see citing works
“98 As in any well-designed study, an initial power calculation is increasingly crucial to evaluate and estimate a sufficient number of samples and avoid a potential waste of resources, especially in such large-scale studies.”
InfluentialTools for Analysis of the Microbiome
Galloway-Peña, J., & Hanson, B. (2020) · Digestive Diseases and Sciences · doi:10.1007/s10620-020-06091-y · Cited by 203 · see citing works
“Current methods for integrating these data types rely upon linking them through a common database, such as UniProt [122] for metagenomics data and metaproteomic data, or ordinating them in multidimensional space [123, 124].”
MethodAI-Driven Drug Discovery: A Comprehensive Review
Ferreira, F. J. N., & Carneiro, A. S. (2025) · ACS Omega · doi:10.1021/acsomega.5c00549 · Cited by 202 · see citing works
Microbial Biodiversity and Bioremediation Assessment Through Omics Approaches
Chandran, H., Meena, M., & Sharma, K. (2020) · Frontiers in Environmental Chemistry · doi:10.3389/fenvc.2020.570326 · Cited by 188 · see citing works
“These technologies have abetted the collection exhaustive biological data about microorganisms, their cellular mechanisms, and biomolecules like enzymes associated with bioremediation (Zhang et al., 2010; Misra et al., 2018; Pandey et al., 2019).”
BackgroundTransforming Clinical Research: The Power of High-Throughput Omics Integration
Vitorino, R. M. P. (2024) · Proteomes · doi:10.3390/proteomes12030025 · Cited by 197 · see citing works
Applications of machine learning in metabolomics: Disease modeling and classification
Galal, A. A., Talal, M., & Moustafa, A. A. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1017340 · Cited by 190 · see citing works
“Metabolomics is a potential solution for this, as it represents the product of the interaction between the various omic layers (Hasin, Seldin, and Lusis 2017; Misra et al., 2018).”
BackgroundMetabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources
Eicher, T., Kinnebrew, G. H., Patt, A. C., Spencer, K., Ying, K. L., Ma, Q., Machiraju, R., & Mathé, E. A. (2020) · Metabolites · doi:10.3390/metabo10050202 · Cited by 185 · see citing works
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
“However, data integration is one of the primary barriers to effective multi-omics research and reaching the desired holistic view of the biological system being studied (Misra et al., 2019).”
BackgroundRelevance of Multi-Omics Studies in Cardiovascular Diseases
León‐Mimila, P., Wang, J., & Huertas‐Vázquez, A. (2019) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2019.00091 · Cited by 152 · see citing works
Multi-omics revolution to promote plant breeding efficiency
Mahmood, U., Li, X., Fan, Y., Chang, W., Niu, Y., Li, J., Qu, C., & Lu, K. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1062952 · Cited by 145 · see citing works
“Since metabolites are typically the main product in complex metabolic cascades, they can link the phenotype with the genome, transcriptome, and proteome (Misra et al., 2018).”
BackgroundGuo, P., Furnary, T., Vasiliou, V., Yan, Q., Nyhan, K., Jones, D. P., Johnson, C. H., & Liew, Z. (2022) · Environment International · doi:10.1016/j.envint.2022.107159 · Cited by 145 · see citing works
Zheng, Y., Liu, Y., Yang, J., Dong, L., Zhang, R., Tian, S., Yu, Y., Ren, L., Hou, W., Zhu, F., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01934-1 · Cited by 142 · see citing works
“…from multiple omics types, aims to combine multiple omics datasets with different modalities from the same set of samples, followed by design of appropriate downstream analysis to identify accurate sample groups or multilayered and interconnected networks of biomolecular features 6,34,40,41 .”
BackgroundSaliva-based microfluidic point-of-care diagnostic
Pittman, T. W., Decsi, D. B., Punyadeera, C., & Henry, C. S. (2023) · Theranostics · doi:10.7150/thno.78872 · Cited by 146 · see citing works
“However, modern technologies (digital PCR, highly sensitive immunoassays) are now able to quantify biomolecules in subpg/mL ranges [93, 94].”
Artificial Intelligence in Pharmaceutical Sciences
Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F., Pan, Z., You, N., Lian, X., Li, F., et al. (2023) · Engineering · doi:10.1016/j.eng.2023.01.014 · Cited by 139 · see citing works
Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
Arjmand, B., Hamidpour, S. K., Tayanloo-Beik, A., Goodarzi, P., Aghayan, H. R., Adibi, H., & Larijani, B. A. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.824451 · Cited by 141 · see citing works
“…evaluating biological samples at each omics research areas (Debnath et al., 2010; Omenn et al., 2012) or make a connection between the resultant data and examining them in a multi-omics fi eld can open new doors to investigate and treat the various diseases (Hasin et al., 2017; Misra et al., 2019).”
Plant Growth-Promoting Bacteria as an Emerging Tool to Manage Bacterial Rice Pathogens
Ngalimat, M. S., Hata, E. M., Zulperi, D. M., Ismail, S. I., Ismail, M. R., Zainudin, N. A. I. M., Saidi, N. B., & Yusof, M. T. (2021) · Microorganisms · doi:10.3390/microorganisms9040682 · Cited by 136 · see citing works
A community resource for paired genomic and metabolomic data mining
Schorn, M., Verhoeven, S., Ridder, L., Huber, F., Acharya, D. D., Aksenov, A. A., Aleti, G., Moghaddam, J. A., Aron, A. T., Aziz, S., et al. (2021) · Nature Chemical Biology · doi:10.1038/s41589-020-00724-z · Cited by 131 · see citing works
Systems biology for crop improvement
Pazhamala, L. T., Kudapa, H., Weckwerth, W., Millar, A. H., & Varshney, R. K. (2021) · The Plant Genome · doi:10.1002/tpg2.20098 · Cited by 133 · see citing works
“Several platforms to integrate multidimensional omics datasets are available (Misra et al., 2019).”
BackgroundMethodArtificial intelligence in metabolomics: a current review
Chi, J., Shu, J., Li, M., Mudappathi, R., Jin, Y., Lewis, F., Boon, A., Qin, X., Liu, L., & Gu, H. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117852 · Cited by 132 · see citing works
Zhang, T., Jiang, B., Xing, Y., Ya, H., Lv, M., & Wang, X. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-18504-8 · Cited by 118 · see citing works
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“Research based on interactomics has revealed new reactions and pathways related to both functionally uncharacterized and well characterized genes, and has provided transformative insights into gene functions and fermentation processes (Gandhi and Shah 2017; Misra et al. 2019).”
From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies
Mukherjee, A., Abraham, S., Singh, A., Balaji, S., & Mukunthan, K. S. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01133-6 · Cited by 124 · see citing works
“The integration of this multi-dimensional omics data into a relevant biological context is challenging due to the volumes of the data and the variances in nomenclature across various data types [182].”
AI in analytical chemistry: Advancements, challenges, and future directions
Rial, R. C. (2024) · Talanta · doi:10.1016/j.talanta.2024.125949 · Cited by 107 · see citing works
Bhatia, S. K., Ahuja, V., Chandel, N., Mehariya, S., Kumar, P., Vinayak, V., Saratale, G. D., Raj, T., Kim, S., & Yang, Y. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127028 · Cited by 104 · see citing works
Yang, S.-Y., Han, S. M., Lee, J., Kim, K. S., Lee, J. E., & Lee, D. (2025) · Journal of Microbiology and Biotechnology · doi:10.4014/jmb.2412.12001 · Cited by 112 · see citing works
“Multi-omics datasets are generated in diverse formats, often lacking universally accepted workflows for integration [202].”
O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J., & Lo, C. H. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.011 · Cited by 107 · see citing works
“Metabolomics and lipidomics datasets are typically acquired from similar biological samples using common analysis methods such as mass spectrometry, ion chromatography, liquid chromatography, and nuclear magnetic resonance [103,106,107].”
MethodAdvances in bulk and single-cell multi-omics approaches for systems biology and precision medicine
Li, Y., Ma, L., Wu, D., & Chen, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab024 · Cited by 101 · see citing works
CustOmics: A versatile deep-learning based strategy for multi-omics integration
Benkirane, H., Pradat, Y., Michiels, S., & Cournède, P.-H. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010921 · Cited by 97 · see citing works
“Due to the genetic complexity of the human molecular profile, omics data generally suffers from the ‘ curse of dimensionality ,’ which is a phenomenon resulting from the high number of features compared to the smaller number of samples [2].”
Ellis, J. L., Jacobs, M., Dijkstra, J., van Laar, H., Cant, J. P., Tulpan, D., & Ferguson, N. D. (2020) · animal · doi:10.1017/s1751731120000312 · Cited by 95 · see citing works
“Mechanistic models used to interpret big ‘omics’ data There is a plethora of ‘omics’ data currently being generated but with minimal biological interpretation of their meaning (data translated into information) (Crow et al., 2019; Misra et al., 2018).”
MethodInterpreting omics data with pathway enrichment analysis
Zhao, K., & Rhee, S. Y. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.01.003 · Cited by 95 · see citing works
Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
Zafar, I., Anwar, S., kanwal, F., Yousaf, W., Nisa, F. U., Kausar, T., Ain, Q. U., Unar, A., Kamal, M. A., Rashid, S., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105263 · Cited by 101 · see citing works
Artificial intelligence for omics data analysis
Ahmed, Z., Wan, S., Zhang, F., & Zhong, W. (2024) · BMC Methods · doi:10.1186/s44330-024-00004-5 · Cited by 92 · see citing works
“This approach not only uncovers hidden patterns but also facilitates the prediction of biological outcomes, thereby accelerating biomedical research and paving the way for personalized medicine [8].”
BackgroundPesticide pestilence: Global scenario and recent advances in detection and degradation methods
Raj, A., Dubey, A., Malla, M. A., & Kumar, A. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117680 · Cited by 91 · see citing works
Interpretation of network-based integration from multi-omics longitudinal data
Bodein, A., Scott‐Boyer, M., Périn, O., Cao, K. L., & Droit, A. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1200 · Cited by 92 · see citing works
“These complex designs are implemented to provide a comprehensive view of biological systems in order to identify complex relationships between omics layers and answer various biological questions such as disease sub-typing, biomarker discovery, models and predictions (2,61– 63).”
BackgroundMulti-omics tools for studying microbial biofilms: current perspectives and future directions
Seneviratne, C. J., Suriyanarayanan, T., Widyarman, A. S., Lee, L. S., Lau, M., Ching, J., Delaney, C., & Ramage, G. (2020) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2020.1828817 · Cited by 89 · see citing works
“Data integration usually employs both statistical and machine learning approaches to reduce the dimensionality of data, facilitate clustering, and predict specific markers/features of interest (Misra et al. 2019).”
BackgroundMethodInfluentialMachine learning: its challenges and opportunities in plant system biology
Hesami, M., Alizadeh, M., Jones, A. M. P., & Torkamaneh, D. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-11963-6 · Cited by 87 · see citing works
“For example, the “curse of dimensionality” (i.e., having much greater multi-omics features ( p ) than observation samples ( n )) is one of the most important problems of the most multi-omics datasets (Misra et al. 2019).”
BackgroundInfluentialOmics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention
Aleksandrova, K., Rodrigues, C. E., Floegel, A., & Ahrens, W. (2020) · Current Obesity Reports · doi:10.1007/s13679-020-00393-y · Cited by 86 · see citing works
“Newer trends include integrative approaches such as multi-omics and trans-omics analyses [13] (Fig.”
BackgroundJuárez, P. D., Hood, D. B., Song, M., & Ramesh, A. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00379 · Cited by 86 · see citing works
“from each analytical platform, lack of standard nomenclature, heterogeneity of data, handling large data volumes, data archiving and sharing with public are other issues (205).”
BackgroundMeasuring the microbiome: Best practices for developing and benchmarking microbiomics methods
Bokulich, N. A., Ziemski, M., Robeson, M. S., & Kaehler, B. D. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.049 · Cited by 86 · see citing works
“Microbiomics has become a large interdisciplinary ‘‘multi-omic” field that incorporates classical microbiology, chemistry, metatranscriptomics, metaproteomics, metabolomics, cultur-omics, ecology, phylogenetics, systems biology, et cetera [33,34].”
BackgroundMethodRecent advances in machine learning applications in metabolic engineering
Patra, P., Disha, B. R., Kundu, P., Das, M., & Ghosh, A. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.108069 · Cited by 87 · see citing works
Raza, A., Salehi, H., Bashir, S., Tabassum, J., Jamla, M., Charagh, S., Barmukh, R., Mir, R. A., Bhat, B. A., Javed, M. A., et al. (2024) · Plant Cell Reports · doi:10.1007/s00299-024-03153-7 · Cited by 88 · see citing works
“4) (Misra et al. 2019; Chao et al. 2023), highlighting the need for dedicated efforts to evaluate and authenticate the characteristics of these resources by scientific communities worldwide.”
BackgroundBig data, integrative omics and network biology
Tolani, P., Gupta, S., Yadav, K., Aggarwal, S., & Yadav, A. K. (2021) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2021.03.006 · Cited by 83 · see citing works
Zhan, C., Dai, Z., Soltanian, M. R., & de Barros, F. P. J. (2022) · Water Resources Research · doi:10.1029/2022wr033241 · Cited by 82 · see citing works
“Such work is already gaining attention in areas related to “Big Data” such as biology (Misra et al., 2019), Internet of Things (IoT) (Karkouch et al.”
BackgroundMulti-omics integration analysis: Tools and applications in environmental toxicology
Shi, C., Cheng, L., Yu, Y., Chen, S., Dai, Y., Yang, J., Zhang, H., Chen, J., & Geng, N. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.124675 · Cited by 82 · see citing works
Multi Omics Applications in Biological Systems
Reyes, C. D. G., Alejo‐Jacuinde, G., Sanchez, B. P., Chávez‐Reyes, J., Onigbinde, S. B., Mogut, D., Hernández‐Jasso, I., Calderón-Vallejo, D., Quintanar, J. L., & Mechref, Y. S. (2024) · Current Issues in Molecular Biology · doi:10.3390/cimb46060345 · Cited by 85 · see citing works
“By enabling regulatory nodes and pathways that could be targeted for intervention, paving the way for personalized medicine and improved healthcare outcomes [14–16].”
BackgroundRecent advancements in microbial-assisted remediation strategies for toxic contaminants
Sharma, P., Bano, A., Singh, S. P., Dubey, N. K., Chandra, R., & Iqbal, H. M. N. (2022) · Cleaner Chemical Engineering · doi:10.1016/j.clce.2022.100020 · Cited by 79 · see citing works
The AI-Powered Evolution of Big Data
Kumar, Y., Marchena, J., Awlla, A. H., Li, J. J., & Abdalla, H. B. (2024) · Applied Sciences · doi:10.3390/app142210176 · Cited by 78 · see citing works
Satrio, R. D., Fendiyanto, M. H., & Miftahudin, M. (2024) · Springer eBooks · doi:10.1007/978-981-97-1699-9_25 · Cited by 77 · see citing works
A Metabolic Perspective and Opportunities in Pharmacologically Important Safflower
Mani, V., Lee, S.-K., Yeo, Y., & Hahn, B.-S. (2020) · Metabolites · doi:10.3390/metabo10060253 · Cited by 75 · see citing works
Metabolomics of plant root exudates: From sample preparation to data analysis
Salem, M. A., Wang, J. Y., & Al‐Babili, S. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1062982 · Cited by 75 · see citing works
“Different approaches for multi-omics data integration alongside their limitation have been extensively reviewed (Fondi and Liò, 2015; Misra et al., 2019; Pinu et al., 2019).”
MethodHassan, S., Bhadwal, S. S., Khan, M., Sabreena, Nissa, K.-U., Shah, R., Bhat, H. M., Bhat, S. A., Lone, I. M., & Ganai, B. A. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.141889 · Cited by 81 · see citing works
Hernández-Baixauli, J., Quesada‐Vázquez, S., Mariné-Casadó, R., Cardoso, K. G., Caimari, A., Bas-Prior, J. M. D., Escoté, X., & Baselga‐Escudero, L. (2020) · Nutrients · doi:10.3390/nu12030806 · Cited by 73 · see citing works
“Ideally, the optimal personalized nutrition should be based in this modern concept of nutrigenomics, but is reasonless in a practical way due to the methodology high cost and the technical difficulties [13].”
OMICS and Other Advanced Technologies in Mycological Applications
Wijayawardene, N., Boonyuen, N., Ranaweera, C., de Zoysa, H. K. S., Padmathilake, R. E., Nifla, F., Dai, D.-Q., Liu, Y., Suwannarach, N., Kumla, J., et al. (2023) · Journal of Fungi · doi:10.3390/jof9060688 · Cited by 58 · see citing works
Sen, P., & Orešič, M. (2023) · Metabolites · doi:10.3390/metabo13070855 · Cited by 74 · see citing works
“Moreover, quality control measures such as outlier removal, artifact correction, and noise filtering are undertaken to improve data quality [1,37–39].”
BackgroundInformation technologies of 21st century and their impact on the society
Yamin, M. (2019) · International Journal of Information Technology · doi:10.1007/s41870-019-00355-1 · Cited by 72 · see citing works
“These tasks are required for the Big data in some of the omics datasets like genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics [6].”
BackgroundIntegrative Network Fusion: A Multi-Omics Approach in Molecular Profiling
Chierici, M., Bussola, N., Marcolini, A., Francescatto, M., Zandonà, A., Trastulla, L., Agostinelli, C., Jurman, G., & Furlanello, C. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01065 · Cited by 75 · see citing works
“Among these methods, the network-based models refer to the reconstruction of a graph representing the complex biological interactions (76, 84), known or predicted, between the variables to discover novel informative relationships (85).”
MethodAmin, A., Zaman, W., & Park, S. (2025) · Genes · doi:10.3390/genes16070809 · Cited by 82 · see citing works
“The disparity in data dimensionality, characterized by one omics layer significantly exceeding the number of features in others, can result in dominance during integrative analyses, thereby obscuring weaker yet critical signals [69].”
InfluentialDemystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
Budhkar, A., Song, Q.-Q., Su, J., & Zhang, X.-H. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.027 · Cited by 71 · see citing works
Omics-based interdisciplinarity is accelerating plant breeding
Shen, Y., Zhou, G., Liang, C., & Tian, Z. (2022) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102167 · Cited by 70 · see citing works
From pharmacogenetics to pharmaco-omics: Milestones and future directions
Auwerx, C., Sadler, M. C., Reymond, A., & Kutalik, Z. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100100 · Cited by 68 · see citing works
Advancing precision oncology with AI-powered genomic analysis
Srivastava, R. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1591696 · Cited by 72 · see citing works
“The emergence of advanced and cost-effective high-throughput technologies (Misra et al., 2018) has generated vast amounts of biological data, ushering in a new era of precision medicine in oncology (Srivastava, 2023a).”
Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome
Peters, D. L., Wang, W., Zhang, X., Ning, Z., Mayne, J., & Figeys, D. (2019) · PROTEOMICS · doi:10.1002/pmic.201800363 · Cited by 64 · see citing works
Applications of Omics Technology for Livestock Selection and Improvement
Chakraborty, D., Sharma, N., Kour, S., Sodhi, S. S., Gupta, M. K., Lee, S., & Son, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.774113 · Cited by 70 · see citing works
Khaleghi, M. K., Savizi, I. S. P., Lewis, N. E., & Shojaosadati, S. A. (2021) · Biotechnology Journal · doi:10.1002/biot.202100212 · Cited by 68 · see citing works
“[124] Second, despite the advances in genome-scale metabolic reconstructions, appropriate high-throughput data are only available for a small group of microorganisms.”
BackgroundOpposite Sides of Pantoea agglomerans and Its Associated Commercial Outlook
Lorenzi, A. S., Bonatelli, M. L., Chia, M. A., Peressim, L., & Quecine, M. C. (2022) · Microorganisms · doi:10.3390/microorganisms10102072 · Cited by 65 · see citing works
Labory, J., Njomgue-Fotso, E., & Bottini, S. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.03.016 · Cited by 63 · see citing works
Understanding the Chemical Exposome During Fetal Development and Early Childhood: A Review
Krausová, M., Braun, D., Buerki-Thurnherr, T., Gundacker, C., Schernhammer, E., Wisgrill, L., & Warth, B. (2023) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-051922-113350 · Cited by 61 · see citing works
Multi-Omics Model Applied to Cancer Genetics
Pettini, F., Visibelli, A., Cicaloni, V., Iovinelli, D., & Spiga, O. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22115751 · Cited by 63 · see citing works
“To achieve this aim, as suggested in the work of [136] there are five essential challenges in the omics integration workflow: (1) experimental challenges, (2) individual omics datasets, (3) integration issues, (4) data issues, and (5) biological knowledge.”
BackgroundExploring the intricacies of plant growth promoting rhizobacteria interactions: an omics review
Mmotla, K., Sibanyoni, N. R., Allie, F., Sitole, L., Mafuna, T., Mashabela, M. D., & Mhlongo, M. I. (2025) · Annals of Microbiology · doi:10.1186/s13213-025-01793-y · Cited by 62 · see citing works
An, A. Y., Choi, K.-Y. G., Baghela, A. S., & Hancock, R. E. W. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.640787 · Cited by 62 · see citing works
“A variety of different integration methods are currently available, primarily for human studies (Lee et al., 2019; Misra et al., 2019), although similar approaches have been recently considered for lactic acid bacteria (O’Donnell et al., 2020).”
BackgroundAlser, M., Lindegger, J., Fırtına, C., Alserr, N. A., Mao, H., Singh, G., Gómez-Luna, J., & Mutlu, O. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.019 · Cited by 59 · see citing works
“The bioinformatics community has developed a multitude of software tools to leverage increasingly large and complex sequencing data-sets [18–20].”
MethodFrom multi-omics approaches to personalized medicine in myocardial infarction
Zhan, C., Tang, T., Wu, E., Zhang, Y., He, M., Wu, R., Bi, C., Wang, J., Zhang, Y., & Shen, B. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1250340 · Cited by 62 · see citing works
Al‐Khelaifi, F., Diboun, I., Donati, F., Botrè, F., Abraham, D., Hingorani, A. D., Albagha, O., Georgakopoulos, C. G., Suhre, K., Yousri, N. A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-56496-7 · Cited by 61 · see citing works
Biosynthesis of Saxitoxin in Marine Dinoflagellates: An Omics Perspective
Akbar, M. A., Yusof, N. Y. M., Tahir, N. I., Ahmad, A., Usup, G., Sahrani, F. K., & Bunawan, H. (2020) · Marine Drugs · doi:10.3390/md18020103 · Cited by 60 · see citing works
Advance computational tools for multiomics data learning
Mansoor, S., Hamid, S., Tuan, T. T., Park, J.-E., & Chung, Y. S. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108447 · Cited by 60 · see citing works
Audouze, K., Sarigiannis, D., Alonso‐Magdalena, P., Brochot, C., Casas, M., Vrijheid, M., Babin, P. J., Karakitsios, S., Coumoul, X., & Barouki, R. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082988 · Cited by 57 · see citing works
Analysis of Nanotoxicity with Integrated Omics and Mechanobiology
Shin, T. H., Nithiyanandam, S., Lee, D. Y., Kwon, D. H., Hwang, J. S., Kim, S. G., Jang, Y. E., Basith, S., Park, S., Mo, J., et al. (2021) · Nanomaterials · doi:10.3390/nano11092385 · Cited by 62 · see citing works
Community Detection in Protein-Protein Interaction Networks and Applications
Manipur, I., Giordano, M., Piccirillo, M., Parashuraman, S., & Maddalena, L. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3138142 · Cited by 56 · see citing works
Cancer chemoprevention: signaling pathways and strategic approaches
Ren, J., Yan, G., Yang, L., Kong, L., Guan, Y., Sun, H., Liu, C., Liu, L., Han, Y., & Wang, X. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02167-1 · Cited by 62 · see citing works
Misra, B. B. (2021) · Analytical Methods · doi:10.1039/d1ay00173f · Cited by 54 · see citing works
Towards a Multi-Omics of Male Infertility
Wagner, A. O., Turk, A., & Kunej, T. (2023) · The World Journal of Men s Health · doi:10.5534/wjmh.220186 · Cited by 54 · see citing works
“Integrated omics approaches have been successfully used in the studies of several diseases, including cancer, infections and age-related diseases [126].”
Integrative Multi-Omics and Artificial Intelligence: A New Paradigm for Systems Biology
Kant, S., Deepika, & Roy, S. (2025) · OMICS A Journal of Integrative Biology · doi:10.1177/15578100251392371 · Cited by 61 · see citing works
“Each dataset in the omics category has its own scale, structure, and noise level, so integration is not an easy process (Babu and Snyder, 2023; Karczewski and Snyder, 2018; Misra et al., 2019).”
- Authors
- Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A.
- Affiliations
- Wake Forest University Health Sciences (US)
- Texas Biomedical Research Institute (US)
- Corresponding author
- Cox, Laura A
- Venue
- Journal of Molecular Endocrinology
- Publisher
- Bioscientifica
- Published
- 2019-01
- Volume
- 62
- Issue
- 1
- Pages
- R21-R45
- ISSN
- 0952-5041 (ISSN-L)
- 1479-6813
- OpenAlex
- W2884682196
- Cited by
- 686
- References
- 191
- Citations by year
- 2026: 120 · 2025: 139 · 2024: 103 · 2023: 84 · 2022: 77 · 2021: 78 · 2020: 59 · 2019: 26
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Molecular Endocrinology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Keywords
- multi-omics integration, data normalization, data cleaning, dimensionality reduction, systems biology, data analysis pipeline, data archiving, data sharing, glycomics, lipidomics, microbiomics, phenomics, high-throughput omics data
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