Skip to main content

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Part of the Methods in Molecular Biology book series (MIMB,volume 2399)

Abstract

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM.

Key words

  • Multi-omics
  • Multimodal
  • Metabolic modeling
  • Flux balance analysis
  • Machine learning
  • Data integration
  • Cancer survival prediction

This is a preview of subscription content, access via your institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-0716-1831-8_5
  • Chapter length: 36 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-1-0716-1831-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Shi Y, Kim S (2014) Towards information analysis for big data. In: 2014 7th conference on Control and automation (CA). IEEE, Piscataway, pp 3–5

    Google Scholar 

  2. Gupta A (2015) Big data analysis using computational intelligence and Hadoop: a study. In: 2015 2nd international conference on computing for sustainable global development (INDIACom). IEEE, Piscataway, pp 1397–1401

    Google Scholar 

  3. Ceri S, Kaitoua A, Masseroli M, Pinoli P, Venco F (2016) Data management for heterogeneous genomic datasets. IEEE/ACM Trans Comput Biol Bioinform 14(6):1251–1264

    PubMed  Google Scholar 

  4. Kench A, Janeja VP, Yesha Y, Rishe N, Grasso MA, Niskar A (2015) Clinico-genomic data analytics for precision diagnosis and disease management. In: 2015 international conference on healthcare informatics (ICHI). IEEE, Piscataway, pp 263–271

    Google Scholar 

  5. Zieba A, Grannas K, Söderberg O, Gullberg M, Nilsson M, Landegren U (2012) Molecular tools for companion diagnostics. New Biotechnol 29(6):634–640

    CAS  Google Scholar 

  6. Ascolani G, Occhipinti A, Liò P (2015) Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer. PLoS Comput Biol 11(5):e1004

    Google Scholar 

  7. Rieger PT (2004) The biology of cancer genetics. In: Seminars in oncology nursing, vol 20. Elsevier, Amsterdam, pp 145–154

    Google Scholar 

  8. Moorcraft SY, Gonzalez D, Walker BA (2015) Understanding next generation sequencing in oncology: a guide for oncologists. Crit Rev Oncol/Hematol 96(3):463–474

    Google Scholar 

  9. Bertram JS (2000) The molecular biology of cancer. Mol Aspects Med 21(6):167–223

    CAS  PubMed  Google Scholar 

  10. Schatz MC, Langmead B (2013) The DNA data deluge. IEEE Spectr 50(7):28–33

    Google Scholar 

  11. Eyassu F, Angione C (2017) Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism. R Soc Open Sci 4(10):170

    Google Scholar 

  12. Pavlova NN, Thompson CB (2016) The emerging hallmarks of cancer metabolism. Cell Metab 23(1):27–47

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Pacheco MP, Bintener T, Sauter T (2019) Towards the network-based prediction of repurposed drugs using patient-specific metabolic models. EBioMedicine 43:26–27

    PubMed  PubMed Central  Google Scholar 

  14. Martin SD, McGee SL (2019) A systematic flux analysis approach to identify metabolic vulnerabilities in human breast cancer cell lines. Cancer Metab 7(1):12

    PubMed  PubMed Central  Google Scholar 

  15. Edwards LM (2017) Metabolic systems biology: a brief primer. J Physiol 595(9):2849–2855

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Palsson B (2015) Systems biology. Cambridge University Press, Cambridge

    Google Scholar 

  17. Angione C (2019) Human systems biology and metabolic modelling: a review—from disease metabolism to precision medicine. BioMed Res Int 2019:8304260

    PubMed  PubMed Central  Google Scholar 

  18. Ryu JY, Kim HU, Lee SY (2017) Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism. Proc Nat Acad Sci 114(45):E9740–E9749

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Angione C (2018) Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism. Bioinformatics 34(3):494–501

    CAS  PubMed  Google Scholar 

  20. Montanari P, Bartolini I, Ciaccia P, Patella M, Ceri S, Masseroli M (2016) Pattern similarity search in genomic sequences. IEEE Trans Knowl Data Eng 28(11):3053–3067

    Google Scholar 

  21. Wang Xl, Li Jy, Liu Y, Wang Yf, Zhao Ds (2013) Building localized bioinformatics platform based on galaxy and high performance computing cluster. In: 2013 6th International Conference on Biomedical engineering and informatics (BMEI). IEEE, Piscataway, pp 712–716

    Google Scholar 

  22. Belgrave D, Henderson J, Simpson A, Buchan I, Bishop C, Custovic A (2017) Disaggregating asthma: big investigation versus big data. J Allergy Clin Immunol 139(2):400–407

    PubMed  PubMed Central  Google Scholar 

  23. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam

    Google Scholar 

  24. Kitchin R (2014) The data revolution: big data, open data, data infrastructures and their consequences. SAGE Publishing, Thousand Oaks

    Google Scholar 

  25. Cairns RA, Harris IS, Mak TW (2011) Regulation of cancer cell metabolism. Nat Rev Cancer 11(2):85

    CAS  PubMed  Google Scholar 

  26. Mardinoglu A, Nielsen J (2016) The impact of systems medicine on human health and disease. Fron Physiol 7:552

    Google Scholar 

  27. Barrett CL, Kim TY, Kim HU, Palsson BØ, Lee SY (2006) Systems biology as a foundation for genome-scale synthetic biology. Curr Opin Biotechnol 17(5):488–492

    CAS  PubMed  Google Scholar 

  28. Yurkovich JT, Palsson BO (2015) Solving puzzles with missing pieces: the power of systems biology. Proc IEEE 104(1):2–7

    Google Scholar 

  29. Palsson BØ (2011) Systems biology: simulation of dynamic network states. Cambridge University Press, Cambridge

    Google Scholar 

  30. Gomez-Cabrero D, Abugessaisa I, Maier D, Teschendorff A, Merkenschlager M, Gisel A, Ballestar E, Bongcam-Rudloff E, Conesa A, Tegnér J (2014) Data integration in the era of omics: current and future challenges. BMC Syst Biol 8(Suppl 2):I1

    PubMed  PubMed Central  Google Scholar 

  31. Ivanov O, van der Schaft A, Weissing FJ (2016) Steady states and stability in metabolic networks without regulation. J Theor Biol 401:78–93

    CAS  PubMed  Google Scholar 

  32. Nielsen J (2017) Systems biology of metabolism: a driver for developing personalized and precision medicine. Cell Metab 25(3):572–579

    CAS  PubMed  Google Scholar 

  33. Joyce AR, Palsson BØ (2006) The model organism as a system: integrating ’omics’ data sets. Nat Rev Mol Cell Biol 7(3):198

    CAS  PubMed  Google Scholar 

  34. Aurich MK, Fleming RM, Thiele I (2016) Metabotools: a comprehensive toolbox for analysis of genome-scale metabolic models. Front Physiol 7:327

    PubMed  PubMed Central  Google Scholar 

  35. Bordbar A, Palsson BO (2012) Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Internal Med 271(2):131–141

    CAS  PubMed  Google Scholar 

  36. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28(3):245

    CAS  PubMed  PubMed Central  Google Scholar 

  37. O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161(5):971–987

    PubMed  PubMed Central  Google Scholar 

  38. Di Filippo M, Colombo R, Damiani C, Pescini D, Gaglio D, Vanoni M, Alberghina L, Mauri G (2016) Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. Comput Biol Chem 62:60–69

    PubMed  Google Scholar 

  39. Vivek-Ananth R, Samal A (2016) Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. Biosystems 147:1–10

    CAS  PubMed  Google Scholar 

  40. Yilmaz LS, Walhout AJ (2017) Metabolic network modeling with model organisms. Curr Opin Chem Biol 36:32–39

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Fernandes S, Robitaille J, Bastin G, Jolicoeur M, Wouwer AV (2016) Dynamic metabolic flux analysis of underdetermined and overdetermined metabolic networks. IFAC-PapersOnLine 49(26):318–323

    Google Scholar 

  42. Rügen M, Bockmayr A, Steuer R (2015) Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA. Sci Rep 5:15,247

    Google Scholar 

  43. Lularevic M, Racher AJ, Jaques C, Kiparissides A (2019) Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions. Biotechnol Bioeng 116(9):2339–2352

    CAS  PubMed  Google Scholar 

  44. Ataman M, Hatzimanikatis V (2015) Heading in the right direction: thermodynamics-based network analysis and pathway engineering. Curr Opin Biotechnol 36:176–182

    CAS  PubMed  Google Scholar 

  45. Willemsen AM, Hendrickx DM, Hoefsloot HC, Hendriks MM, Wahl SA, Teusink B, Smilde AK, van Kampen AH (2015) MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Mol BioSyst 11(1):137–145

    CAS  PubMed  Google Scholar 

  46. Zhang Y, Rajapakse JC (2009) Machine learning in bioinformatics, vol 4. Wiley, London

    Google Scholar 

  47. Leung MK, Delong A, Alipanahi B, Frey BJ (2016) Machine learning in genomic medicine: a review of computational problems and data sets. Proc IEEE 104(1):176–197

    Google Scholar 

  48. Angermueller C, Pärnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Mol Syst Biol 12(7):878

    PubMed  PubMed Central  Google Scholar 

  49. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Briefings Bioinform 18(5):851–869

    Google Scholar 

  50. Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16(6):321

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, et al (2018) Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15(141):20170

    Google Scholar 

  52. Zeng ISL, Lumley T (2018) Review of statistical learning methods in integrated omics studies (an integrated information science). Bioinform Biol Insights 12:1177932218759

    Google Scholar 

  53. Horgan RP, Kenny LC (2011) ‘omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynaecol 13(3):189–195

    Google Scholar 

  54. Biedendieck R, Borgmeier C, Bunk B, Stammen S, Scherling C, Meinhardt F, Wittmann C, Jahn D (2011) Systems biology of recombinant protein production using bacillus megaterium. In: Methods in enzymology, vol 500. Elsevier, Amsterdam, pp 165–195

    Google Scholar 

  55. Fondi M, Liò P (2015) Multi-omics and metabolic modelling pipelines: challenges and tools for systems microbiology. Microbiol Res 171:52–64

    CAS  PubMed  Google Scholar 

  56. Yurkovich JT, Palsson BO (2018) Quantitative-omic data empowers bottom-up systems biology. Curr Opin Biotechnol 51:130–136

    CAS  PubMed  Google Scholar 

  57. Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038

    Google Scholar 

  58. Vijayakumar S, Conway M, Lió P, Angione C (2018) Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Briefings Bioinform 19(6):1218–1235

    CAS  Google Scholar 

  59. Serra A, Fratello M, Fortino V, Raiconi G, Tagliaferri R, Greco D (2015) MVDA: a multi-view genomic data integration methodology. BMC Bioinform 16(1):261

    Google Scholar 

  60. Zampieri G, Vijayakumar S, Yaneske E, Angione C (2019) Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 15(7):e1007

    Google Scholar 

  61. Sertbas M, Ulgen KO (2018) Unlocking human brain metabolism by genome-scale and multiomics metabolic models: relevance for neurology research, health, and disease. OMICS: J Integr Biol 22(7):455–467

    CAS  Google Scholar 

  62. Culley C, Vijayakumar S, Zampieri G, Angione C (2020) A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc Nat Acad Sci 117(31):18,869–18,879

    CAS  Google Scholar 

  63. Tong L, Mitchel J, Chatlin K, Wang MD (2020) Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis. BMC Med Inform Decis Making 20(1):1–12

    Google Scholar 

  64. Jhajharia S, Verma S, Kumar R (2016) Predictive analytics for breast cancer survivability: a comparison of five predictive models. In: Proceedings of the second international conference on information and communication technology for competitive strategies. ACM, New York, p 26

    Google Scholar 

  65. Ma Z, Krings AW (2008) Survival analysis approach to reliability, survivability and prognostics and health management (PHM). In: 2008 IEEE aerospace conference. IEEE, Piscataway, pp 1–20

    Google Scholar 

  66. Iuliano A, Occhipinti A, Angelini C, De Feis I, Lió P (2016) Cancer markers selection using network-based Cox regression: a methodological and computational practice. Front Physiol 7:208

    PubMed  PubMed Central  Google Scholar 

  67. Iuliano A, Occhipinti A, Angelini C, De Feis I, Liò P (2018) Combining pathway identification and breast cancer survival prediction via screening-network methods. Front Genet 9:206

    PubMed  PubMed Central  Google Scholar 

  68. Lee C, Zame WR, Yoon J, van der Schaar M (2018) Deephit: a deep learning approach to survival analysis with competing risks. In: AAAI, pp 2314–2321

    Google Scholar 

  69. Wang P, Li Y, Reddy CK (2019) Machine learning for survival analysis: a survey. ACM Comput Surv 51(6):1–36

    Google Scholar 

  70. Zupan B, DemšAr J, Kattan MW, Beck JR, Bratko I (2000) Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artif Intell Med 20(1):59–75

    CAS  PubMed  Google Scholar 

  71. Harrell Jr FE, Lee KL, Califf RM, Pryor DB, Rosati RA (1984) Regression modelling strategies for improved prognostic prediction. Stat Med 3(2):143–152

    PubMed  Google Scholar 

  72. Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1–3

    Google Scholar 

  73. Kleinbaum DG, Klein M (2010) Survival analysis. Springer, Berlin

    Google Scholar 

  74. Nisbet R, Elder J, Miner G (2009) Basic algorithms for data mining: a brief overview. In: Handbook of statistical analysis and data mining applications, pp 121–150

    Google Scholar 

  75. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge. https://www.deeplearningbook.org

    Google Scholar 

  76. Xu J, Wu P, Chen Y, Meng Q, Dawood H, Dawood H (2019) A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data. BMC Bioinform 20(1):1–11

    Google Scholar 

  77. Lemsara A, Ouadfel S, Fröhlich H (2020) Pathme: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data. BMC Bioinform 21:1–20

    Google Scholar 

  78. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:13126114

    Google Scholar 

  79. Simidjievski N, Bodnar C, Tariq I, Scherer P, Andres Terre H, Shams Z, Jamnik M, Liò P (2019) Variational autoencoders for cancer data integration: design principles and computational practice. Front Genet 10:1205

    PubMed  PubMed Central  Google Scholar 

  80. Liang M, Li Z, Chen T, Zeng J (2014) Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinform 12(4):928–937

    Google Scholar 

  81. Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M (2019) Moli: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35(14):i501–i509

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Cheerla A, Gevaert O (2019) Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14):i446–i454

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Chen R, Yang L, Goodison S, Sun Y (2020) Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics 36(5):1476–1483

    CAS  PubMed  Google Scholar 

  84. Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FC, Clarke D, Gu M, Emani P, Yang YT, et al. (2018) Comprehensive functional genomic resource and integrative model for the human brain. Science 362(6420):eaat8464

    Google Scholar 

  85. Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S, et al (2020) Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143(6):1920–1933

    PubMed  PubMed Central  Google Scholar 

  86. Cuperlovic-Culf M (2018) Machine learning methods for analysis of metabolic data and metabolic pathway modeling. Metabolites 8(1):4

    PubMed Central  Google Scholar 

  87. Vijayakumar S, Conway M, Lió P, Angione C (2018) Optimization of multi-omic genome-scale models: methodologies, hands-on tutorial, and perspectives. In: Metabolic network reconstruction and modeling. Springer, Berlin, pp 389–408

    Google Scholar 

  88. Lawson C, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, et al (2021) Machine learning for metabolic engineering: a review. Metab Eng 63(1):34–60

    CAS  PubMed  Google Scholar 

  89. Ben Guebila M, Thiele I (2019) Predicting gastrointestinal drug effects using contextualized metabolic models. PLoS Comput Biol 15(6):e1007,100

    CAS  Google Scholar 

  90. Guo W, Xu Y, Feng X (2017) Deepmetabolism: a deep learning system to predict phenotype from genome sequencing. arXiv preprint arXiv:170503094

    Google Scholar 

  91. Ajjolli Nagaraja A, Fontaine N, Delsaut M, Charton P, Damour C, Offmann B, Grondin-Perez B, Cadet F (2019) Flux prediction using artificial neural network (ANN) for the upper part of glycolysis. PloS One 14(5):e0216,178

    Google Scholar 

  92. Occhipinti A, Eyassu F, Rahman TJ, Rahman PK, Angione C (2018) In silico engineering of pseudomonas metabolism reveals new biomarkers for increased biosurfactant production. PeerJ 6:e6046

    PubMed  PubMed Central  Google Scholar 

  93. Yaneske E, Angione C (2018) The poly-omics of ageing through individual-based metabolic modelling. BMC Bioinform 19(14):83–96

    Google Scholar 

  94. Yang JH, Wright SN, Hamblin M, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, et al. (2019) A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell 177(6):1649–1661

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Vijayakumar S, Rahman PKMSM, Angione C (2020) A hybrid flux balance analysis and machine learning pipeline elucidates the metabolic response of cyanobacteria to different growth conditions. iScience 23(12):101818

    Google Scholar 

  96. Kavvas ES, Yang L, Monk JM, Heckmann D, Palsson BO (2020) A biochemically-interpretable machine learning classifier for microbial GWAS. Nat Commun 11(1):1–11

    Google Scholar 

  97. Occhipinti A, Hamadi Y, Kugler H, Wintersteiger C, Yordanov B, Angione C (2020) Discovering essential multiple gene effects through large scale optimization: an application to human cancer metabolism. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2020.2973386

  98. Zhang J, Petersen SD, Radivojevic T, Ramirez A, Pérez-Manríquez A, Abeliuk E, Sánchez BJ, Costello Z, Chen Y, Fero MJ, et al. (2020) Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat Commun 11(1):1–13

    Google Scholar 

  99. Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdóttir HS, Wachowiak J, Keating SM, Vlasov V, et al. (2019) Creation and analysis of biochemical constraint-based models using the cobra toolbox v. 3.0. Nat Protoc 14(3):639–702

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Angione C, Conway M, Lió P (2016) Multiplex methods provide effective integration of multi-omic data in genome-scale models. BMC Bioinform 17(4):257–269

    Google Scholar 

  101. Tian M, Reed JL (2018) Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis. Bioinformatics 34(22):3882–3888

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the support from UKRI Research England’s THYME project, from the Children’s Liver Disease Foundation, and from Earlier.org.

Author Contributions

Conceptualization: S.V. and C.A.; Data curation: S.V., G.M. and A.O.; Formal analysis: S.V., G.M., P.M. and A.O.; Funding Acquisition: C.A.; Investigation: S.V., G.M., P.M. and A.O.; Methodology: S.V., G.M., P.M., A.O. and C.A.; Project administration: S.V. and C.A.; Resources: S.V., G.M., P.M. and A.O.; Software: S.V., G.M., P.M., A.O. and C.A.; Supervision: S.V. and C.A.; Validation: S.V., G.M. and A.O.; Visualization: S.V.; Writing—original draft: S.V., G.M., P.M., A.O. and C.A.; Writing—reviewing and editing: S.V., G.M., A.O. and C.A.

Declaration of Interests The authors declare no competing interests.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Claudio Angione .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply

About this protocol

Verify currency and authenticity via CrossMark

Cite this protocol

Vijayakumar, S., Magazzù, G., Moon, P., Occhipinti, A., Angione, C. (2022). A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. In: Cortassa, S., Aon, M.A. (eds) Computational Systems Biology in Medicine and Biotechnology. Methods in Molecular Biology, vol 2399. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1831-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1831-8_5

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1830-1

  • Online ISBN: 978-1-0716-1831-8

  • eBook Packages: Springer Protocols