Biological insights through omics data integration

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

Biopython: freely available python tools for computational molecular biology and bioinformatics

Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080 · see citing works

mixOmics: An R package for ‘omics feature selection and multiple data integration

Rohart, F., Gautier, B., Singh, A., & Lê Cao, K.-A. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005752 · Cited by 4,056 · see citing works

Machine Learning Applications in Cancer Prognosis and Prediction

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338 · see citing works

Gut microbiome structure and metabolic activity in inflammatory bowel disease

Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall, A. B., Mallick, H., McIver, L. J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0306-4 · Cited by 1,995 · see citing works

Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets

Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works

A Whole-Cell Computational Model Predicts Phenotype from Genotype

Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012) · Cell · doi:10.1016/j.cell.2012.05.044 · Cited by 1,300 · see citing works

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works

Next-Generation Machine Learning for Biological Networks

Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works

Dynamic flux balance analysis of diauxic growth in Escherichia coli

Mahadevan, R., Edwards, J. S., & Doyle, F. J. (2002) · Biophysical Journal · doi:10.1016/s0006-3495(02)73903-9 · Cited by 945 · see citing works

Multiplexed quantification of proteins and transcripts in single cells

Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832 · see citing works

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Machado, D., Andrejev, S., Tramontano, M., & Patil, K. R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky537 · Cited by 724 · see citing works

Machine learning and its applications to biology

Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656 · see citing works

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication

Piazza, I., Kochanowski, K., Cappelletti, V., Fuhrer, T., Noor, Ε., Sauer, U., & Picotti, P. (2018) · Cell · doi:10.1016/j.cell.2017.12.006 · Cited by 547 · see citing works

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

Sánchez, B. J., Zhang, C., Nilsson, A., Lahtvee, P., Kerkhoven, E. J., & Nielsen, J. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167411 · Cited by 466 · see citing works

Methods for the integration of multi-omics data: mathematical aspects

Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0857-9 · Cited by 449 · see citing works

Quantitative and logic modelling of molecular and gene networks

Novère, N. G. L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3885 · Cited by 422 · see citing works

44th IEEE Conference on Decision and Control and European Control Conference

(2011) · IEEE Control Systems · doi:10.1109/mcs.2011.942938 · Cited by 415 · see citing works

Predictive non-linear modeling of complex data by artificial neural networks

Almeida, J. S. (2002) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(02)00288-4 · Cited by 332 · see citing works

Proteoforms as the next proteomics currency

Smith, L. M., & Kelleher, N. L. (2018) · Science · doi:10.1126/science.aat1884 · Cited by 322 · see citing works

Systems-level analysis of mechanisms regulating yeast metabolic flux

Hackett, S. R., Zanotelli, V. R., Xu, W., Goya, J., Park, J. O., Perlman, D. H., Gibney, P. A., Botstein, D., Storey, J. D., & Rabinowitz, J. D. (2016) · Science · doi:10.1126/science.aaf2786 · Cited by 297 · see citing works

Systems proteomics of liver mitochondria function

Williams, E. G., Wu, Y., Jha, P., Dubuis, S., Blattmann, P., Argmann, C. A., Houten, S. M., Amariuta, T., Wolski, W., Zamboni, N., et al. (2016) · Science · doi:10.1126/science.aad0189 · Cited by 289 · see citing works

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro <i>k</i> _cat measurements

Davidi, D., Noor, E., Liebermeister, W., Bar-Even, A., Flamholz, A., Tummler, K., Barenholz, U., Goldenfeld, M., Shlomi, T., & Milo, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1514240113 · Cited by 266 · see citing works

Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers

Yugi, K., Kubota, H., Hatano, A., & Kuroda, S. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2015.12.013 · Cited by 258 · see citing works

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Stein-O’Brien, G., Arora, R., Culhane, A. C., Favorov, A. V., Garmire, L. X., Greene, C. S., Goff, L. A., Li, Y., Ngom, A., Ochs, M. F., et al. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.07.003 · Cited by 235 · see citing works

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Khodayari, A., & Maranas, C. D. (2016) · Nature Communications · doi:10.1038/ncomms13806 · Cited by 230 · see citing works

Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks

Lee, J. M., Gianchandani, E. P., Eddy, J. A., & Papin, J. A. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000086 · Cited by 225 · see citing works

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Heckmann, D., Lloyd, C. J., Mih, N., Ha, Y., Zielinski, D. C., Haiman, Z. B., Desouki, A. A., Lercher, M. J., & Palsson, B. O. (2018) · Nature Communications · doi:10.1038/s41467-018-07652-6 · Cited by 218 · see citing works

Nontargeted Metabolomics Reveals the Multilevel Response to Antibiotic Perturbations

Zampieri, M., Zimmermann, M., Claassen, M., & Sauer, U. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.04.002 · Cited by 188 · see citing works

Central dogma rates and the trade-off between precision and economy in gene expression

Hausser, J., Mayo, A., Keren, L., & Alon, U. (2019) · Nature Communications · doi:10.1038/s41467-018-07391-8 · Cited by 188 · see citing works

COBRAme: A computational framework for genome-scale models of metabolism and gene expression

Lloyd, C. J., Ebrahim, A., Yang, L., King, Z. A., Catoiu, E., O’Brien, E. J., Liu, J. K., & Palsson, B. O. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006302 · Cited by 167 · see citing works

Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data

Gerosa, L., van Rijsewijk, B. R. B. H., Christodoulou, D., Kochanowski, K., Schmidt, T., Noor, Ε., & Sauer, U. (2015) · Cell Systems · doi:10.1016/j.cels.2015.09.008 · Cited by 157 · see citing works

Next-generation genome-scale models for metabolic engineering

King, Z. A., Lloyd, C. J., Feist, A. M., & Palsson, B. O. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.12.016 · Cited by 157 · see citing works

Multi-omic data integration enables discovery of hidden biological regularities

Ebrahim, A., Brunk, E., Tan, J., O'Brien, E. J., Kim, D., Szubin, R., Lerman, J. A., Lechner, A., Sastry, A., Bordbar, A., et al. (2016) · Nature Communications · doi:10.1038/ncomms13091 · Cited by 155 · see citing works

Multi-omics integration—a comparison of unsupervised clustering methodologies

Tini, G., Marchetti, L., Priami, C., & Scott‐Boyer, M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx167 · Cited by 147 · see citing works

Biological insights through nontargeted metabolomics

Sévin, D. C., Kuehne, A., Zamboni, N., & Sauer, U. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.10.001 · Cited by 134 · see citing works

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

Haas, R., Zelezniak, A., Iacovacci, J., Kamrad, S., Townsend, S., & Ralser, M. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.08.009 · Cited by 131 · see citing works

Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli

Kochanowski, K., Gerosa, L., Brunner, S. F., Christodoulou, D., Nikolaev, Y., & Sauer, U. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167402 · Cited by 128 · see citing works

Genomewide landscape of gene–metabolome associations in Escherichia coli

Fuhrer, T., Zampieri, M., Sévin, D. C., Sauer, U., & Zamboni, N. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167150 · Cited by 114 · see citing works

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Reimers, A.-M., Knoop, H., Bockmayr, A., & Steuer, R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617508114 · Cited by 113 · see citing works

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

Stanford, N., Lubitz, T., B, K. S. A., Klipp, E., Mendes, P., & Liebermeister, W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079195 · Cited by 109 · see citing works

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

Zelezniak, A., Vowinckel, J., Capuano, F., Messner, C. B., Demichev, V., Polowsky, N., Mülleder, M., Kamrad, S., Klaus, B., Keller, M. A., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.08.001 · Cited by 100 · see citing works

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis

Salvy, P., Fengos, G., Ataman, M., Pathier, T., Soh, K. C., & Vassily Hatzimanikatis, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty499 · Cited by 97 · see citing works

Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics

Bordbar, A., McCloskey, D., Zielinski, D. C., Sonnenschein, N., Jamshidi, N., & Palsson, B. Ø. (2015) · Cell Systems · doi:10.1016/j.cels.2015.10.003 · Cited by 94 · see citing works

GAM: a web-service for integrated transcriptional and metabolic network analysis

Sergushichev, A. A., Loboda, A. A., Jha, A. K., Vincent, E. E., Driggers, E. M., Jones, R. G., Pearce, E. J., & Artyomov, M. N. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw266 · Cited by 94 · see citing works

Correction: Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks

Lee, J. M., Gianchandani, E. P., Eddy, J. A., & Papin, J. A. (2008) · PLoS Computational Biology · doi:10.1371/annotation/5594348b-de00-446a-bdd0-ec56e70b3553 · Cited by 89 · see citing works

Are multi-omics enough?

Vilanova, C., & Porcar, M. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.101 · Cited by 82 · see citing works

Lessons on enzyme kinetics from quantitative proteomics

Davidi, D., & Milo, R. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.02.007 · Cited by 72 · see citing works

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

DiMucci, D., Kon, M., & Segrè, D. (2018) · mSystems · doi:10.1128/msystems.00181-18 · Cited by 71 · see citing works

Statistical single cell multi-omics integration

Colomé‐Tatché, M., & Theis, F. J. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.01.003 · Cited by 70 · see citing works

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

Yang, L., Ebrahim, A., Lloyd, C. J., Saunders, M. A., & Palsson, B. Ø. (2019) · BMC Systems Biology · doi:10.1186/s12918-018-0675-6 · Cited by 58 · see citing works

Toward Community Standards and Software for Whole-Cell Modeling

Waltemath, D., Karr, J. R., Bergmann, F., Chelliah, V., Hucka, M., Krantz, M., Liebermeister, W., Mendes, P., Myers, C. J., Pir, P., et al. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2560762 · Cited by 57 · see citing works

Large-Scale Metabolic Models: From Reconstruction to Differential Equations

B, K. S. A., & Mendes, P. (2013) · Industrial Biotechnology · doi:10.1089/ind.2013.0003 · Cited by 56 · see citing works

Towards detecting regulatory protein–metabolite interactions

Diether, M., & Sauer, U. (2017) · Current Opinion in Microbiology · doi:10.1016/j.mib.2017.07.006 · Cited by 55 · see citing works

Memote: A community driven effort towards a standardized genome-scale metabolic model test suitePreprint

Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F., Ataman, M., Babaei, P., Bartell, J. A., Blank, L. M., Chauhan, S., Correia, K., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/350991 · Cited by 55 · see citing works

SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis

Zimmermann‐Kogadeeva, M., & Zamboni, N. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005109 · Cited by 49 · see citing works

Proteomic and Metabolomic Characterization of a Mammalian Cellular Transition from Quiescence to Proliferation

Lee, H., Jedrychowski, M. P., Vinayagam, A., Wu, N., Shyh‐Chang, N., Hu, Y., Min-Wen, C., Moore, J. K., Asara, J. M., Lyssiotis, C. A., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.074 · Cited by 49 · see citing works

Integrating –omics data into genome-scale metabolic network models: principles and challenges

Ramon, C., Gollub, M. G., & Stelling, J. (2018) · Essays in Biochemistry · doi:10.1042/ebc20180011 · Cited by 45 · see citing works

Integration of multi-omics data of a genome-reduced bacterium: Prevalence of post-transcriptional regulation and its correlation with protein abundances

Chen, W.-H., van Noort, V., Lluch‐Senar, M., Hennrich, M. L., Wodke, J. A. H., Yus, E., Alibés, A., Roma, G., Mende, D. R., Pesavento, C., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw004 · Cited by 40 · see citing works

Prediction of synergistic drug combinations

Weinstein, Z., Bender, A., & Cokol, M. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.05.005 · Cited by 37 · see citing works

Envisioning the future of ‘big data’ biomedicine

Bui, A. A. T., & Horn, J. D. V. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.03.017 · Cited by 36 · see citing works

Crosstalk between transcription and metabolism: how much enzyme is enough for a cell?

Donati, S., Sander, T., & Link, H. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1396 · Cited by 30 · see citing works

Metabolism as a signal generator across trans-omic networks at distinct time scales

Yugi, K., & Kuroda, S. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.12.002 · Cited by 29 · see citing works

The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions?

Tummler, K., & Klipp, E. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.11.003 · Cited by 28 · see citing works

Comprehensive mathematical model of oxidative phosphorylation valid for physiological and pathological conditions

Heiske, M., Letellier, T., & Klipp, E. (2017) · FEBS Journal · doi:10.1111/febs.14151 · Cited by 25 · see citing works

Integration of metabolic, regulatory and signaling networks towards analysis of perturbation and dynamic responses

Chiappino-Pepe, A., Pandey, V., Ataman, M., & Vassily Hatzimanikatis, A. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.01.007 · Cited by 15 · see citing works

Integration of large-scale metabolic, signaling, and gene regulatory networks with application to infection responses

Richard, G., Chang, H., Cizelj, I., Belta, C. A., Julius, A. A., & Amar, S. (2011) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2011.6160954 · Cited by 7 · see citing works

DynamicME: Dynamic simulation and refinement of integrated models of metabolism and protein expressionPreprint

Yang, L., Ebrahim, A., Lloyd, C. J., Saunders, M. A., & Palsson, B. Ø. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/319962 · Cited by 5 · see citing works

Dynamic Flux Balance Analysis Models in SBMLPreprint

König, M., Watanabe, L. H., Grzegorzewski, J., & Myers, C. (2018) · bioRxiv · doi:10.1101/245076 · Cited by 4 · see citing works

Central dogma rates and the trade-off between precision and economyPreprint

Hausser, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17632/2vbrg3w4p3 · Cited by 4 · see citing works

Common principles and best practices for engineering microbiomes

Lawson, C. E., Harcombe, W. R., Hatzenpichler, R., Lindemann, S. R., Löffler, F. E., O'Malley, M. A., Martín, H. G., Pfleger, B. F., Raskin, L. M., Venturelli, O. S., et al. (2019) · Nature Reviews Microbiology · doi:10.1038/s41579-019-0255-9 · Cited by 645 · see citing works

Recent advances in the application of metabolomics for food safety control and food quality analyses

Li, S., Tian, Y., Jiang, P., Lin, Y., Liu, X., & Yang, H. (2020) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2020.1761287 · Cited by 395 · see citing works

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Liebal, U. W., An, P. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020) · Metabolites · doi:10.3390/metabo10060243 · Cited by 371 · see citing works

Gut microbiome, big data and machine learning to promote precision medicine for cancer

Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., Gasbarrini, A., & Tortora, G. (2020) · Nature Reviews Gastroenterology &amp; Hepatology · doi:10.1038/s41575-020-0327-3 · Cited by 324 · see citing works

Tools 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

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

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

Bernstein, D. B., Sulheim, S., Almaas, E., & Segrè, D. (2021) · Genome biology · doi:10.1186/s13059-021-02289-z · Cited by 153 · see citing works

Fermented food products in the era of globalization: tradition meets biotechnology innovations

Galimberti, A., Bruno, A., Agostinetto, G., Casiraghi, M., Guzzetti, L., & Labra, M. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.10.006 · Cited by 137 · see citing works

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Murmu, S., Sinha, D., Chaurasia, H., Sharma, S., Das, R., Jha, G. K., & Archak, S. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1292054 · Cited by 136 · see citing works

The Multifunctions and Future Prospects of Endophytes and Their Metabolites in Plant Disease Management

Xia, Y., Liu, J., Chen, C., Mo, X., Tan, Q., He, Y. H., Wang, Z., Yin, J., & Zhou, G. (2022) · Microorganisms · doi:10.3390/microorganisms10051072 · Cited by 125 · see citing works

Optimization and Scale-Up of Fermentation Processes Driven by Models

Du, Y., Wang, M.-Y., Yang, L., Tong, L., Guo, D., & Ji, X. (2022) · Bioengineering · doi:10.3390/bioengineering9090473 · Cited by 130 · see citing works

Systematic identification of metabolites controlling gene expression in E. coli

Lempp, M., Farke, N., Kuntz, M. A., Freibert, S., Lill, R., & Link, H. (2019) · Nature Communications · doi:10.1038/s41467-019-12474-1 · Cited by 113 · see citing works

Network analyses in microbiome based on high-throughput multi-omics data

Liu, Z., Ma, A., Mathé, E. A., Merling, M. R., Ma, Q., & Liu, B. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa005 · Cited by 90 · see citing works

Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications

Zhang, X., Ibrahim, Z., Khaskheli, M. B., Raza, H., Zhou, F., & Shamsi, I. H. (2024) · Sustainability · doi:10.3390/su16177651 · Cited by 90 · see citing works

Microfluidic cultivation and analysis tools for interaction studies of microbial co-cultures

Burmeister, A., & Grünberger, A. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.09.001 · Cited by 85 · see citing works

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

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

Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications

MacDonald, M. A., Nöbel, M., Roche Recinos, D., Martínez, V. S., Schulz, B. L., Howard, C. B., Baker, K., Shave, E., Lee, Y. Y., Marcellin, E., et al. (2022) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2021.1998821 · Cited by 55 · see citing works

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

Biswas, A., Kumari, A., Gaikwad, D. S., & Pandey, D. K. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2023.0197 · Cited by 56 · see citing works

Multi-omics data integration approaches for precision oncology

Aguila, R. C., Alonso-Pupo, N., & Hernández‐Rodríguez, E. W. (2022) · Molecular Omics · doi:10.1039/d1mo00411e · Cited by 48 · see citing works

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

Tong, H., Küken, A., & Nikoloski, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-16279-5 · Cited by 46 · see citing works

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Chung, C. H., Lin, D., Eames, A., & Chandrasekaran, S. (2021) · Metabolites · doi:10.3390/metabo11090606 · Cited by 47 · see citing works

Hostile Takeover: How Viruses Reprogram Prokaryotic Metabolism

Jacobson, T. B., Callaghan, M. M., & Amador‐Noguez, D. (2021) · Annual Review of Microbiology · doi:10.1146/annurev-micro-060621-043448 · Cited by 40 · see citing works

Novel Secondary Ion Mass Spectrometry Methods for the Examination of Metabolic Effects at the Cellular and Subcellular Levels

Bonnin, E. A., & Rizzoli, S. O. (2020) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2020.00124 · Cited by 37 · see citing works

Heterogeneous data integration methods for patient similarity networks

Gliozzo, J., Mesiti, M., Notaro, M., Petrini, A., Patak, A., Gallardo, A. P., Paccanaro, A., Valentini, G., & Casiraghi, E. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac207 · Cited by 36 · see citing works

Implementing Computational Modeling in Tissue Engineering: Where Disciplines Meet

Post, J., Loerakker, S., Merks, R., & Carlier, A. (2022) · Tissue Engineering. Part A · doi:10.1089/ten.tea.2021.0215 · Cited by 30 · see citing works

Automated calibration for stability selection in penalised regression and graphical models

Bodinier, B., Filippi, S. L., Nøst, T. H., Chiquet, J., & Chadeau‐Hyam, M. (2023) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlad058 · Cited by 36 · see citing works

Towards next-generation model microorganism chassis for biomanufacturing

Liu, Y., Su, A., Li, J., Ledesma‐Amaro, R., Xu, P., Du, G., & Liu, L. (2020) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-020-10902-7 · Cited by 31 · see citing works

Combined morphological and proteome profiling reveals target-independent impairment of cholesterol homeostasis

Schneidewind, T., Brause, A., Schölermann, B., Sievers, S., Pahl, A., Sankar, M. G., Winzker, M., Janning, P., Kumar, K., Ziegler, S., et al. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.06.003 · Cited by 29 · see citing works

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Holbrook‐Smith, D., Trouillon, J., & Sauer, U. (2023) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-030722-021957 · Cited by 27 · see citing works

SLGCN: Structure-enhanced line graph convolutional network for predicting drug–disease associations

Liu, B.-M., Gao, Y.-L., Li, F., Zheng, C.-H., & Liu, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111187 · Cited by 23 · see citing works

Translational Bioinformatics Applied to the Study of Complex Diseases

Casotti, M. C., Meira, D. D., Alves, L. N. R., Bessa, B. G. D. O., Campanharo, C. V., Vicente, C. R., Aguiar, C. C., Duque, D. D. A., Barbosa, D. G., dos Santos, E. D. V. W., et al. (2023) · Genes · doi:10.3390/genes14020419 · Cited by 25 · see citing works

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

Arbas, S. M., Busi, S. B., Queirós, P., de Nies, L., Herold, M., May, P., Wilmes, P., Muller, E. E. L., & Narayanasamy, S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.666244 · Cited by 20 · see citing works

An automated workflow for multi-omics screening of microbial model organisms

Donati, S., Mattanovich, M., Hjort, P., Jacobsen, S. A. B., Blomquist, S. D., Mangaard, D., Gurdo, N., Pastor, F. P., Maury, J., Hanke, R., et al. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00277-6 · Cited by 19 · see citing works

Integrating host and microbiome biology using holo-omics

Kobel, C. M., Merkesvik, J., Burgos, I. M. T., Lai, W., Øyås, O., Pope, P. B., Hvidsten, T. R., & Aho, V. T. E. (2024) · Molecular Omics · doi:10.1039/d4mo00017j · Cited by 17 · see citing works

Representation Learning for the Clustering of Multi-Omics Data

Viaud, G., Mayilvahanan, P., & Cournède, P.-H. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3060340 · Cited by 17 · see citing works

A systematic mapping study of semantic technologies in multi-omics data integration

Filippis, G. M. D., Amalfitano, D., Russo, C., Tommasino, C., & Rinaldi, A. M. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104809 · Cited by 19 · see citing works

Metabolism as a signal generator in bacteria

Ledezma-Tejeida, D., Schastnaya, E., & Sauer, U. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100404 · Cited by 16 · see citing works

Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data

Gawthrop, P. J., Pan, M., & Crampin, E. J. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0478 · Cited by 15 · see citing works

Integrated metabolomics and transcriptomics analysis of roots of Bupleurum chinense and B. scorzonerifolium, two sources of medicinal Chaihu

Wan, H., Zhou, L., Wu, B., Han, W., Sui, C., & Wei, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-27019-8 · Cited by 13 · see citing works

Using resource constraints derived from genomic and proteomic data in metabolic network models

Becker, K. D., Totis, N., Bernaerts, K., & Waldherr, S. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100400 · Cited by 13 · see citing works

AI‐Driven Omics for Smart Remediation of Heavy Metal Contaminated Soils

Gul, I., Adil, M. F., Lu, H., Lu, S., Li, H., Liu, F., Cao, L., Han, Z., Bashir, S., Hussain, M. M., et al. (2025) · Physiologia Plantarum · doi:10.1111/ppl.70611 · Cited by 11 · see citing works

The Key Metabolic Network and Genes Regulating the Fresh Fruit Texture of Jujube (Ziziphus jujuba Mill.) Revealed via Metabolomic and Transcriptomic Analysis

Song, S., Jin, J., Li, M., Kong, D., Cao, M., Wang, X., Li, Y., Chen, X., Zhang, X., Pang, X., et al. (2023) · Plants · doi:10.3390/plants12112087 · Cited by 11 · see citing works

Single-cell omics analysis with genome-scale metabolic modeling

Chen, Y., Gustafsson, J., Yang, J., Nielsen, J., & Kerkhoven, E. J. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103078 · Cited by 11 · see citing works

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

Razalli, I. I., Abdullah‐Zawawi, M., Tamizi, A. A., Harun, S., Zainal‐Abidin, R., Jalal, M. I. A., Ullah, M. A., & Zainal, Z. (2025) · Planta · doi:10.1007/s00425-025-04666-5 · Cited by 11 · see citing works

Branching out: what omics can tell us about primate evolution

Kuderna, L. F. K., Esteller-Cucala, P., & Marquès‐Bonet, T. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.06.006 · Cited by 12 · see citing works

Genetic and epigenetic regulation of natural resistance to HIV-1 infection: new approaches to unveil the HESN secret

Fenizia, C., Saulle, I., Clerici, M., & Biasin, M. (2020) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2020.1732820 · Cited by 11 · see citing works

Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism

Pries, C., Razaghi‐Moghadam, Z., Kopka, J., & Nikoloski, Z. (2021) · Scientific Reports · doi:10.1038/s41598-021-84114-y · Cited by 11 · see citing works

Genome-scale flux balance analysis reveals redox trade-offs in the metabolism of the thermoacidophile Methylacidiphilum fumariolicum under auto-, hetero-and methanotrophic conditions

Saldivar, A., Ruiz‐Ruiz, P., Revah, S., & Zúñiga, C. (2024) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1291612 · Cited by 9 · see citing works

Network-Based Approaches for the Investigation of Microbial Community Structure And Function Using Metagenomics-Based Data

Kaiser, T., Jahansouz, C., & Staley, C. M. (2022) · Future Microbiology · doi:10.2217/fmb-2021-0219 · Cited by 9 · see citing works

Combined HTR1A/1B methylation and human functional connectome to recognize patients with MDD

Xu, Z., Gao, C., Tan, T., Jiang, W., Wang, T., Chen, Z., Shen, T., Chen, L., Tang, H., Chen, W., et al. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114842 · Cited by 8 · see citing works

Reliable biological and multi-omics research through biometrology

Dong, L., Zhang, Y., Fu, B., Swart, C., Jiang, H., Liu, Y., Huggett, J. F., Wielgosz, R., Niu, C., Li, Q., et al. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05239-3 · Cited by 8 · see citing works

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Agostinetto, G., Anna, S., Antonia, B., Dario, P., & Casiraghi, M. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.794547 · Cited by 8 · see citing works

A supervised machine learning workflow for the reduction of highly dimensional biological data

Andersen, L. K., & Reading, B. J. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100090 · Cited by 8 · see citing works

Extracting functionally accurate context-specific models of Atlantic salmon metabolism

Molversmyr, H., Øyås, O., Rotnes, F., & Vik, J. O. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00280-x · Cited by 8 · see citing works

Cross-Disciplinary Genomics Approaches to Studying Emerging Fungal Infections

Ghosh, P. N., Brookes, L. M., Edwards, H. M., Fisher, M. C., Jervis, P., Kappel, D., Sewell, T. R., Shelton, J., Skelly, E., & Rhodes, J. L. (2020) · Life · doi:10.3390/life10120315 · Cited by 7 · see citing works

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data

Ma, W., Chen, S., Qi, Y., Song, M., Zhai, J., Zhang, T., Xie, S. B., Wang, G., & Ma, C. (2022) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-022-00522-2 · Cited by 7 · see citing works

How Can We Fully Realize the Potential of Mathematical and Biological Models to Reintegrate Biology?

Dornhaus, A., Smith, B. H., Hristova, K. A., & Buckley, L. B. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab142 · Cited by 5 · see citing works

Robust Data Integration Method for Classification of Biomedical Data

Polewko-Klim, A., Mnich, K., & Rudnicki, W. R. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01718-7 · Cited by 4 · see citing works

Modular Dynamic Biomolecular Modelling with Bond Graphs: The Unification of Stoichiometry, Thermodynamics, Kinetics and DataPreprint

Gawthrop, P. J., Pan, M., & Crampin, E. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.24.436792 · Cited by 4 · see citing works

A Robust Isotope Ratio LC–MS/MS Workflow for High-Throughput Metabolic Profiling of Bacteria

Rocha, C., Pinto, S. P., Jensen, S. I., Nielsen, L. K., & Donati, S. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c04931 · Cited by 3 · see citing works

An automated workflow for multi-omics screening of microbial model organismsPreprint

Donati, S., Mattanovich, M., Hjort, P., Jacobsen, S. A. B., Blomquist, S. D., Mangaard, D., Gurdo, N., Pastor, F. P., Maury, J., Hanke, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.18.500181 · Cited by 3 · see citing works

Meta-omic and machine learning approaches to understand microbial cross talks

Bombaywala, S., & Dafale, N. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91631-8.00008-1 · Cited by 2 · see citing works

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 4 · see citing works

METABOLOMIC APPROACHES IN STUDYING OF CARDIOVASCULAR DISEASES

Abdullaev, A. A., Абдуллаева, Г. Ж., & Юсупова, Х. (2021) · Eurasian heart journal · doi:10.38109/2225-1685-2021-1-106-117 · Cited by 2 · see citing works

Data libraries – the missing element for modeling biological systems

Baryshnikova, A. (2020) · FEBS Journal · doi:10.1111/febs.15261 · Cited by 2 · see citing works

The Effect of Sensor Fusion on Data-Driven Learning of Koopman OperatorsPreprint

Balakrishnan, S., Hasnain, A., Egbert, R. G., & Yeung, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15091 · Cited by 2 · see citing works

High-throughput Plant Metabolomics and Predictive Modelling

Santos, M. C. B., Mirande‐Ney, C., Dussarrat, T., Marti, G., Bouchereau, A., Dellero, Y., Prigent, S., & Pétriacq, P. (2025) · CABI biotechnology series. · doi:10.1079/9781800626911.0003 · Cited by 1 · see citing works

Quantitative trait locus mapping in placenta: A comparative study of chorionic villus and birth placenta

Dieckmann, L., Lahti‐Pulkkinen, M., Cruceanu, C., Räikkönen, K., Binder, E. B., & Czamara, D. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100326 · Cited by 1 · see citing works

easyMF: A Web Platform for Matrix Factorization-based Biological Discovery from Large-scale Transcriptome DataPreprint

Ma, W., Chen, S., Zhai, J., Qi, Y., Xie, S. B., Song, M., & Ma, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.21.405563 · Cited by 1 · see citing works

Automated calibration for stability selection in penalised regression and graphical models: a multi-OMICs network application exploring the molecular response to tobacco smokingPreprint

Bodinier, B., Filippi, S. L., Nøst, T. H., Chiquet, J., & Chadeau‐Hyam, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02521 · Cited by 1 · see citing works

Impact of perinatal factors on the maternal- neonatal microbiota and influence on health outcomes

Selma‐Royo, M. (2020) · doi:10.4995/thesis/10251/153159

Drug Targeting

Longden, J., Cardinali, A., & Telford‐Cooke, L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821618-7.00030-4

Time course gene expression experiments

Martínez, O. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91810-7.00014-5

Improving SWATH-MS data analysis using multi-omics approach

Singh, M. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.236964

Molecular Fingerprints-Based Machine Learning for Metabolic Profiling

Sirocchi, C., Biancucci, F., Suffian, M., Benedetti, R., Donati, M., Ferretti, S., Bogliolo, A., Magnani, M., Menotta, M., & Montagna, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74640-6_8

Applications of machine learning in kombucha fermentation process

Gallegos‐Infante, J. A., & González‐Laredo, R. F. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13297-1.00008-5

A toolkit for quantifying individual response to herbal extracts in metabolic and inflammatory stress

Park, S. Y., Kwon, O., van den Broek, T., Bouwman, J., & Kim, J. Y. (2025) · npj Science of Food · doi:10.1038/s41538-024-00354-y

Exposome within the Context of the Public Health Perspectives

El‐Gilany, A. (2025) · Journal of Comprehensive Health · doi:10.25259/jch_62_2024

Additional file 1 of Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

Bernstein, D. B., Sulheim, S., Almaas, E., & Segrè, D. (2021) · Figshare · doi:10.6084/m9.figshare.14060009

Artificial Intelligence-Assisted Omics Techniques in Plant Defense: Recent Advancements and Future Prospects

Anas, M., Basri, S., Chaurasiya, N., & Ali, M. (2026) · CABI biotechnology series. · doi:10.1079/9781800628649.0007

Modélisation mathématique de la dégradation des ARNm bactériens et intégration de données omiques

Etienne, T. A. (2020) · theses.fr (ABES) · doi:10.70675/e7b998faz57e4z4a39z9dabz35df7fb4537c

Toward Metabolomics Analyses With Combined Capillary Vibrating Sharp‐Edge Spray Ionization and Atmospheric Pressure Chemical Ionization

Pursell, M. E., Ojanuga, A., Banerjee, C., Ezeigbo, E., Rahman, M., Dewasurendra, V. K., Hughart, T. B., Johnson, M. B., Sultana, M. N., Guo, X., et al. (2026) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.70110

Diagnosing Anaerobic Digesters' Function and Performance: From Meta‐Omics to Integrated Meta‐Omics Analyses

Ehiosun, K. I., Chapleur, O., & Mazéas, L. (2026) · Environmental Microbiology Reports · doi:10.1111/1758-2229.70417

Unlocking biological complexity: the role of machine learning in integrative multi-omics

Kumar, R., Ruhel, R., & van Wijnen, A. J. (2024) · Academia Biology · doi:10.20935/acadbiol7428 · Cited by 10 · see citing works

Methods for the integration of multi-omics data: mathematical aspects

Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0857-9 · Cited by 449 · see citing works

Multi-omics: An Opportunity to Dive into Systems Biology

dos Santos, E. A. F., Cruz, E. C. S., Ribeiro, H. C., Barbosa, L. D., Zandonadi, F. D. S., & Sussulini, A. (2020) · Brazilian Journal of Analytical Chemistry · doi:10.30744/brjac.2179-3425.rv-03-2020 · Cited by 5 · see citing works

The multiomics revolution in the era of deep learning: Allies or enemies?

Labory, J., & Bottini, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00017-x · Cited by 1 · see citing works

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