Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

The Perseus computational platform for comprehensive analysis of (prote)omics data

Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803

Network biology: understanding the cell's functional organization

Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903

The Large-Scale Organization of Metabolic Networks

Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972

Finishing the euchromatic sequence of the human genome

International Human Genome Sequencing Consortium (2004) · Nature · doi:10.1038/nature03001 · Cited by 4,971

Coming of age: ten years of next-generation sequencing technologies

Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352

Optimization Methods for Large-Scale Machine Learning

Bottou, L., Curtis, F. E., & Nocedal, J. M. (2018) · SIAM Review · doi:10.1137/16m1080173 · Cited by 3,573

Central Dogma of Molecular Biology

CRICK, F. (1970) · Nature · doi:10.1038/227561a0 · Cited by 3,444

Multilayer networks

Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235

The structure and dynamics of multilayer networks

Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125

Identification of bioactive metabolites using activity metabolomics

Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0108-4 · Cited by 1,008

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

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

Applications 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

Escherichia coli as a host for metabolic engineering

Pontrelli, S., Chiu, T., Lan, E. I., Chen, F. Y.-H., Chang, P., & Liao, J. C. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.04.008 · Cited by 397

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 373

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities

Jiang, D., Armour, C. R., Hu, C., Meng, M., Tian, C., Sharpton, T. J., & Jiang, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00995 · Cited by 170

The rhizosphere microbiome: Significance in rhizoremediation of polyaromatic hydrocarbon contaminated soil

Kotoky, R., Rajkumari, J., & Pandey, P. (2018) · Journal of Environmental Management · doi:10.1016/j.jenvman.2018.04.022 · Cited by 128

Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application

Shahrajabian, M. H., & Sun, W. (2023) · Current Pharmaceutical Analysis · doi:10.2174/1573412919666230406100948 · Cited by 116

Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

Zanfardino, M., Franzese, M., Pane, K., Cavaliere, C., Monti, S., Esposito, G., Salvatore, M., & Aiello, M. (2019) · Journal of Translational Medicine · doi:10.1186/s12967-019-2073-2 · Cited by 99

Biological insights through omics data integration

Noor, E., Cherkaoui, S., & Sauer, U. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.007 · Cited by 83

Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition

Vowinckel, J., Zelezniak, A., Kibler, A., Bruderer, R., Mülleder, M., Reiter, L., & Ralser, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-22610-4 · Cited by 78

Integrating imaging and omics data: A review

Antonelli, L., Guarracino, M. R., Maddalena, L., & Sangiovanni, M. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.04.032 · Cited by 87

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Vijayakumar, S., Rahman, P. K. S. M., & Angione, C. (2020) · iScience · doi:10.1016/j.isci.2020.101818 · Cited by 69

Integrating multi-omics data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study

Rodosthenous, T., Shahrezaei, V., & Evangelou, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa530 · Cited by 67

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Abbasi, E. Y., Deng, Z., Arain, Q. A., Khan, A., Shaikh, A., Reshan, M. S. A., Sulaiman, A. A., & Alshahrani, H. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25369 · Cited by 49

Is it time to get rid of black boxes and cultivate trust in AI?

Gastounioti, A., & Kontos, D. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200088 · Cited by 47

Increase Crop Resilience to Heat Stress Using Omic Strategies

Zhou, R., Jiang, F., Niu, L., Song, X., Yu, L., Yang, Y., & Wu, Z. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.891861 · Cited by 47

Yeast optimizes metal utilization based on metabolic network and enzyme kinetics

Chen, Y., Li, F., Mao, J., Chen, Y., & Nielsen, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020154118 · Cited by 46

Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data

Mallik, S., & Zhao, Z. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby120 · Cited by 46

A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus

Larras, F., Billoir, É., Scholz, S., Tarkka, M. T., Wubet, T., Delignette‐Muller, M. L., & Schmitt‐Jansen, M. (2020) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2020.122727 · Cited by 51

An Overview of the Genetics and Genomics of the Urochloa Species Most Commonly Used in Pastures

Ferreira, R. C. U., Moraes, A. D. C. L., Chiari, L., Simeão, R. M., Vigna, B. B. Z., & de Souza, A. P. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.770461 · Cited by 42

Proteomics as a tool to disclose the cellular and molecular mechanisms of selected anticancer gold compounds

Gamberi, T., Pratesi, A., Luigi Messori, A., & Massai, L. (2021) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2021.213905 · Cited by 40

Editorial: Using Genomics, Metagenomics and Other “Omics” to Assess Valuable Microbial Ecosystem Services and Novel Biotechnological Applications

Marco, D. E., & Abram, F. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00151 · Cited by 39

Bioinformatics applied to biotechnology: A review towards bioenergy research

de Carvalho, L. M., Borelli, G., Camargo, A. P., de Assis, M. A., de Ferraz, S. M. F., Fiamenghi, M. B., José, J., Mofatto, L. S., Nagamatsu, S. T., Persinoti, G. F., et al. (2019) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2019.02.016 · Cited by 37

Strategies and challenges for metabolic rewiring

Yu, T., Dabirian, Y., Liu, Q., Siewers, V., & Nielsen, J. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.004 · Cited by 35

Multi-omics characterization of the necrotrophic mycoparasite Saccharomycopsis schoenii

Junker, K., Chailyan, A., Hesselbart, A., Forster, J., & Wendland, J. (2019) · PLOS Pathogens · doi:10.1371/journal.ppat.1007692 · Cited by 34

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Vlachavas, E., Bohn, J. R., Ückert, F., & Nürnberg, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062822 · Cited by 37

Molecular Systems Biology of Neurodevelopmental Disorders, Rett Syndrome as an Archetype

Faúndez, V., Wynne, M. E., Crocker, A. J., & Tarquinio, D. C. (2019) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2019.00030 · Cited by 34

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging

Whitwell, H. J., Bacalini, M. G., Blyuss, O. B., Chen, S., Garagnani, P., Gordleeva, S. Y., Jalan, S., Ivanchenko, M., Игоревич, К. О., Кустикова, В. Д., et al. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00136 · Cited by 34

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Karaman, E. D., & Işık, Z. (2023) · Medical Sciences · doi:10.3390/medsci11030044 · Cited by 31

Regulation of gene expression through protein-metabolite interactions

Hornisch, M., & Piazza, I. (2025) · npj Metabolic Health and Disease · doi:10.1038/s44324-024-00047-w · Cited by 33

Protective Effects and Mechanism of Polysaccharides from Edible Medicinal Plants in Alcoholic Liver Injury: A Review

Su, Z., Yan, T.-Y., Feng, J., Zhang, M., Han, L., Zhang, H., & Xiao, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242216530 · Cited by 28

Evolution of Artificial Intelligence-Powered Technologies in Biomedical Research and Healthcare

Díaz-Flores, E., Meyer, T., & Giorkallos, A. (2022) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2021_189 · Cited by 29

Advances in omics research on peanut response to biotic stresses

Huang, R., Li, H., Gao, C., Yu, W., & Zhang, S. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1101994 · Cited by 26

Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

Ramiro, L., García‐Berrocoso, T., Briansó, F., Goicoechea, L., Simats, A., Llombart, V., Gonzalo, R., Hainard, A., Martínez-Sáez, E. A., Canals, F., et al. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02401-1 · Cited by 24

Use of omic technologies in early life gastrointestinal health and disease: from bench to bedside

Beck, L. C., Granger, C. L., Masi, A. C., & Stewart, C. J. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1922278 · Cited by 24

Metabolic Dynamics in Short- and Long-Term Microgravity in Human Primary Macrophages

Thiel, C. S., Vahlensieck, C., Bradley, T., Tauber, S., Lehmann, M., & Ullrich, O. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22136752 · Cited by 20

Omics Notebook: robust, reproducible and flexible automated multiomics exploratory analysis and reporting

Blum, B. C., & Emili, A. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab024 · Cited by 21

New Insights on the Beneficial Effects of the Probiotic Kefir on Vascular Dysfunction in Cardiovascular and Neurodegenerative Diseases

Vasquez, E. C., Aires, R., Ton, A. M. M., & Amorim, F. G. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666200304145224 · Cited by 21

Recent omics-based computational methods for COVID-19 drug discovery and repurposing

Tayara, H., Abdelbaky, I., & Chong, K. T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab339 · Cited by 21

Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases

Trapečar, M. (2021) · FEBS Letters · doi:10.1002/1873-3468.14260 · Cited by 21

‘Omic’ technologies as a helpful tool in radioecological research

Volkova, P., & Geras'kin, S. A. (2018) · Journal of Environmental Radioactivity · doi:10.1016/j.jenvrad.2018.04.011 · Cited by 20

Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010–2021

Cui, X., Chang, Y., Yang, C., Cong, Z., Wang, B., & Leng, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010050 · Cited by 19

Recent advances in experimental design of synthetic microbial communities for biocontrol application

Karmakar, B., Thakuria, D., Begum, R. H., & Joga, R. J. (2025) · BioControl · doi:10.1007/s10526-024-10295-w · Cited by 19

Identification of Differential Responses of Goat PBMCs to PPRV Virulence Using a Multi-Omics Approach

Eloiflin, R.-J., Auray, G., Python, S., Rodrigues, V., Sevéno, M., Urbach, S., Koulali, K. E., Holzmüller, P., Totté, P., Libeau, G., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.745315 · Cited by 19

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin, V., Bodein, A., Scott‐Boyer, M., Leclercq, M., Périn, O., & Droit, A. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.962799 · Cited by 19

Gene communities in co-expression networks across different tissues

Russell, M., Aqil, A., Saitou, M., Gökçümen, Ö., & Masuda, N. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011616 · Cited by 18

Neonatal microbiome in the multiomics era: development and its impact on long-term health

Neu, J., & Stewart, C. J. (2025) · Pediatric Research · doi:10.1038/s41390-025-03953-x · Cited by 20

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Gopalakrishnan, S., Johnson, W., Valderrama-Gómez, M. Á., Içten, E., Tat, J., Lay, F. D., Diep, J., Gomez, N. V., Stevens, J. L., Schlegel, F., et al. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.07.009 · Cited by 17

Compartment and hub definitions tune metabolic networks for metabolomic interpretations

Waller, T. C., Berg, J. A., Lex, A., Chapman, B. E., & Rutter, J. P. (2020) · GigaScience · doi:10.1093/gigascience/giz137 · Cited by 15

Big Data in Gastroenterology Research

Alizadeh, M. A., Moura, N. S., Schledwitz, A., Patil, S. A., Ravel, J., & Raufman, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032458 · Cited by 17

Multivariate Statistical Methods for High-Dimensional Multiset Omics Data Analysis

Csala, A., & Zwinderman, A. H. (2019) · Codon Publications eBooks · doi:10.15586/computationalbiology.2019.ch5 · Cited by 17

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants

Gao, Z., Lu, Y., Li, M., Chong, Y., Hong, J., Wu, J., Wu, D., Xi, D., & Deng, W. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25179271 · Cited by 15

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Murgia, F., Gagliano, A., Tanca, M. G., Or‐Geva, N., Hendren, A. J., Carucci, S., Pintor, M., Cera, F., Cossu, F., Sotgiu, S., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.645267 · Cited by 14

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass

Malinowska, M., Ruud, A. K., Jensen, J., Svane, S. F., Smith, A. G., Bellucci, A., Lenk, I., Nagy, I., Fois, M., Didion, T., et al. (2022) · The Plant Genome · doi:10.1002/tpg2.20253 · Cited by 14

Pan-Omics in Sheep: Unveiling Genetic Landscapes

Li, M., Lu, Y., Gao, Z., Yue, D., Hong, J., Wu, J., Xi, D., Deng, W., & Chong, Y. (2024) · Animals · doi:10.3390/ani14020273 · Cited by 15

The Rhizosphere Microbiome and Its Role in Plant Growth in Stressed Conditions

Bhuyan, B., Debnath, S., & Pandey, P. (2020) · Microorganisms for sustainability · doi:10.1007/978-981-15-9154-9_21 · Cited by 12

IMFLer: A Web Application for Interactive Metabolic Flux Analysis and Visualization

Petrovs, R., Stalidzāns, E., & Pentjuss, A. (2021) · Journal of Computational Biology · doi:10.1089/cmb.2021.0056 · Cited by 11

Structural Predictive Model of Presenilin-2 Protein and Analysis of Structural Effects of Familial Alzheimer’s Disease Mutations

Soto-Ospina, A., Marín, P. A., Bedoya, G., & Lanau, A. V. (2021) · Biochemistry Research International · doi:10.1155/2021/9542038 · Cited by 14

Advances in mass spectrometry for clinical analysis: Data acquisition, interpretation and information integration

Zhang, H., Ouyang, Z., & Zhang, W. (2023) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2023.117380 · Cited by 11

Investigating plant responses to microgravity and adaptations in gravisensitive environments

Farooq, M., Ali, S., Khan, M., Jang, Y., Kim, E., Zhao, D.-D., & Kim, K. (2024) · Environmental Sciences Europe · doi:10.1186/s12302-024-00851-7 · Cited by 10

MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation

Madrid, L., Rubio-Escudero, C., Pontes, B., González‐Pérez, A., Riquelme, J. C., & Sáez, M. E. (2022) · Applied Sciences · doi:10.3390/app12083987 · Cited by 11

A Comprehensive Insight of Current and Future Challenges in Large-Scale Soil Microbiome Analyses

Legeay, J., & Hijri, M. (2022) · Microbial Ecology · doi:10.1007/s00248-022-02060-2 · Cited by 10

Experimental infrastructure requirements for quantitative research on microbial communities

Kleerebezem, R., Stouten, G. R., Koehorst, J. J., Langenhoff, A. A. M., Schaap, P. J., & Smidt, H. (2021) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2021.01.017 · Cited by 9

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictionsPreprint

Osseni, M. A., Tossou, P., Laviolette, F., & Corbeil, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.14.516459 · Cited by 10

AI-Generated Nanoparticles in Immunological Research

Ali, K. A., Choudhuri, S., Roy, S. K., Khandekar Hussan, R. E. Z. A., Mandal, D., Chakraborty, R., & Hossain, C. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch011 · Cited by 8

A Picture Worth a Thousand Molecules—Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

Minehart, J. A., & Speer, C. M. (2021) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2020.615059 · Cited by 9

A Comprehensive Review on the Development of Foodomics-Based Approaches to Evaluate the Quality Degradation of Different Food Products

Zhang, R., Jia, W., & Shi, L. S. (2022) · Food Reviews International · doi:10.1080/87559129.2022.2077362 · Cited by 7

Cheminformatics and Bioinformatics at the Interface with Systems Biology

Kaushik, A. C., Mehmood, A., Wei, D., Nawab, S., Sahi, S., & Kumar, A. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166037 · Cited by 7

Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets

Iacovacci, J., Peluso, A., Ebbels, T. M. D., Ralser, M., & Glen, R. C. (2020) · Metabolites · doi:10.3390/metabo10110435 · Cited by 7

Exploring genetics and genomics trends to understand the link between secondary metabolic genes and agronomic traits in cereals under stress

Aggarwal, P., Muthamilarasan, M., & Choudhary, P. (2024) · Journal of Plant Physiology · doi:10.1016/j.jplph.2024.154379 · Cited by 6

Mass spectrometry for mitochondrial multi-omics

Wang, Z., Liu, B., Lin, L., & Qiao, L. (2023) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2023.117063 · Cited by 8

An Integrated Gaussian Graphical Model to evaluate the impact of exposures on metabolic networks

Lee, J. W., Moen, E. L., Punshon, T., Hoen, A. G., Stewart, D. A., Li, H., Karagas, M. R., & Gui, J. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103417 · Cited by 6

Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets

Iacovacci, J., Peluso, A., Ebbels, T. M. D., Ralser, M., & Glen, R. C. (2020) · Figshare · doi:10.17863/cam.59380 · Cited by 6

IonFlow: a galaxy tool for the analysis of ionomics data sets

Iacovacci, J., Lin, W., Griffin, J. L., & Glen, R. C. (2021) · Metabolomics · doi:10.1007/s11306-021-01841-z · Cited by 6

Bluster or Lustre: Can AI Improve Crops and Plant Health?

Gardiner, L., & Krishna, R. (2021) · Plants · doi:10.3390/plants10122707 · Cited by 7

Kernel Based Approaches to Identify Hidden Connections in Gene Networks Using NetAnalyzer

Jabato, F. M., Rojano, E., Perkins, J. R., Ranea, J. A. G., & Seoane, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45385-5_68 · Cited by 5

Integrating Mindsets and Toolsets at the Frontier of Systems Biology

Yoram Vodovotz, B. (2021) · Frontiers in Systems Biology · doi:10.3389/fsysb.2021.745692 · Cited by 5

Large-scale integration of DNA methylation and gene expression array platforms identifies both cis and trans relationships

Lancaster, E. E., Vladimirov, V. I., Riley, B. P., Landry, J. W., Roberson‐Nay, R., & York, T. P. (2022) · Epigenetics · doi:10.1080/15592294.2022.2079293 · Cited by 5

Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra

Buric, F., Zrimec, J., & Zelezniak, A. (2020) · Patterns · doi:10.1016/j.patter.2020.100137 · Cited by 5

How is Big Data reshaping preclinical aging research?

Fernández, M. E., Martínez-Romero, J., Aon, M. A., Bernier, M., Price, N. L., & de Cabo, R. (2023) · Lab Animal · doi:10.1038/s41684-023-01286-y · Cited by 5

Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data

Angarita-Rodríguez, A., Mejía, N. M., González, J., Papin, J. A., Aristizábal-Pachón, A. F., & Pinzón, A. M. (2025) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1500710 · Cited by 4

Secondary metabolism of Microcystis : current understanding and recent advances in unlocking genomic and chemical diversity

Yancey, C. E., Hart, L. N., & Dick, G. J. (2026) · Applied and Environmental Microbiology · doi:10.1128/aem.01634-25 · Cited by 3

An integrative network-based approach to identify driving gene communities in chronic obstructive pulmonary disease

Marino, R., Aalamat, Y. E., Bol, V., Caselle, M., Giudice, G. D., Lambert, C., Medini, D., Wilkinson, T. M. A., & Muzzi, A. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00425-6 · Cited by 4

Pinpointing the integration of artificial intelligence in liver cancer immune microenvironment

Bukhari, I., Li, M., Li, G., Xu, J., Zheng, P., & Chu, X. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1520398 · Cited by 3

Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware

Shallom, D., Naiger, D., Weiss, S., & Tuller, T. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00415 · Cited by 3

DESIGN AND DEVELOPMENT OF A PHARMACOGENOMIC MODEL FOR BREAST CANCER TO STUDY THE VARIATION IN DRUG ACTION AND SIDE EFFECTS

M., H. V. A., & Namboori, P. K. K. (2022) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2022v14i3.44356 · Cited by 3

MOT: a Multi-Omics Transformer for Multiclass Classification Tumour Types PredictionsPreprint

Osseni, M. A., Tossou, P., Laviolette, F., & Corbeil, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1348696/v1 · Cited by 4

Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma

Li, W., Muneer, A., Waqas, M., Zhou, X., & Wu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73360-4_6 · Cited by 2

Tensor-Based Approaches for Omics Data Analysis: Applications, Challenges, and Future Directions

Khoshnam, A., Chafamo, D., & Tokcan, N. (2025) · La Matematica · doi:10.1007/s44007-025-00149-x · Cited by 2

Multi-omics analysis of thermogenic lipolysis in brown adipocytes

Mattanovich, M., Anagho, H. A., Argemi‐Muntadas, L., Nielsen, M. L., Gao, Q., & Möritz, T. (2025) · iScience · doi:10.1016/j.isci.2025.113382 · Cited by 5

Metabolic Responses to Arsenite Exposure Regulated through Histidine Kinases PhoR and AioS in Agrobacterium tumefaciens 5A

Rawle, R. A., Tokmina‐Lukaszewska, M., Shi, Z., Kang, Y., Tripet, B., Fang, D., Wang, G., McDermott, T. R., Copié, V., & Bothner, B. B. (2020) · Microorganisms · doi:10.3390/microorganisms8091339 · Cited by 2

Specialty Grand Challenge: Data and Model Integration in Systems Biology

Saccenti, E. (2021) · Frontiers in Systems Biology · doi:10.3389/fsysb.2021.800894 · Cited by 2

Metabolomes evolve faster than metabolic network structures

Chen, Y., & Li, F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2400519121 · Cited by 2

RettDb: the Rett syndrome omics database to navigate the Rett syndrome genomic landscape

Cillari, N., Neri, G., Pisanti, N., Milazzo, P., & Borello, U. (2024) · Database · doi:10.1093/database/baae109 · Cited by 2

Phytophthora alni Infection Reinforces the Defense Reactions in Alnus glutinosa-Frankia Roots to the Detriment of Nodules

Vincent, M., Boubakri, H., Fournier, P., Parisot, N., Pétriacq, P., Cassan, C., Flandin, A., Miotello, G., Armengaud, J., Hay, A., et al. (2025) · Molecular Plant-Microbe Interactions · doi:10.1094/mpmi-12-24-0160-r · Cited by 2

The Use of In Silico Genome-Scale Models for the Rational Design of Minimal Cells

Lachance, J., Rodrigue, S., & Palsson, B. Ø. (2019) · Springer eBooks · doi:10.1007/978-3-030-31897-0_6 · Cited by 2

Molecular correlates of childhood adversity – a multi-omics perspective on stress regulation

Zang, J. C. S., May, C., Marcus, K., & Kumsta, R. (2025) · Stress · doi:10.1080/10253890.2025.2495918 · Cited by 3

Biomarkers and advances in AML-MRC: from bench to bedside

Meng, F., Al-Khafaji, Z., Muzammil, K., Sarbazjoda, E., Khaksari, M. N., Bayani, A., & Samarkhazan, H. S. (2026) · Annals of Hematology · doi:10.1007/s00277-026-06805-8 · Cited by 1

Bioinformatics Databases and Tools for Analysis of Multi-omics

Pham, C. A., Ngo, A. D., Bui, N. L., & Chu, D. (2024) · Springer eBooks · doi:10.1007/978-981-97-1844-3_4 · Cited by 1

A Customizable Analysis Flow in Integrative Multi-Omics

Lancaster, S. M., Sanghi, A., Wu, S., & Snyder, M. P. (2020) · Biomolecules · doi:10.3390/biom10121606 · Cited by 29

A Comprehensive Protocol and Step-by-Step Guide for Multi-Omics Integration in Biological Research

Adam, E., Zanoaga, M. D., Rota, R., & Cominetti, O. (2025) · Journal of Visualized Experiments · doi:10.3791/66995 · Cited by 4

Biological insights through omics data integration

Noor, E., Cherkaoui, S., & Sauer, U. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.007 · Cited by 83

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