SpringerLink
Log in

HER-Omics, a Model of Transcriptomics Data Integration in EHRs

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 713))

  • 238 Accesses

Abstract

Technological and computational advancements used to generate and process large sets of biological data (omics data) are driving a critical change in the study of biomedical sciences and medicine. While genomics, transcriptomics, and proteomics, coupled with bioinformatics and biostatistics, are gaining momentum, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. The need for this type of data in a clinical setup is becoming more evident. Hence, integrating omics data in electronic health records (EHRs) is a basis for advancing toward personalized diagnosis and treatments. However, due to the heavy and complex nature of omics data, it is still an ongoing challenge to attempt to integrate them into EHRs in an easily interpretable manner. To this end, we present HER-Omics (HERO), a tool that combines both the patients’ EHR and their Omics data. HERO is lightweight, simple to use, and can be integrated with an existing EHR database. The workflow involves taking raw FASTQ files as input, performing the analysis, and giving a clear and readable output that will aid in the decision-making process. We illustrate the use of this tool by using demi transcriptomics data. This shows a proof of concept that we hope will be developed in a more advanced manner in the near future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 245.03
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 320.99
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Collins, F.S., Varmus, H.: A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015). https://doi.org/10.1056/NEJMp1500523

    Article  Google Scholar 

  2. Fernald, G.H., Capriotti, E., Daneshjou, R., Karczewski, K.J., Altman, R.B.: Bioinformatics challenges for personalized medicine. Bioinforma. Oxf. Engl. 27, 1741–1748 (2011). https://doi.org/10.1093/bioinformatics/btr295

    Article  Google Scholar 

  3. Katsnelson, A.: Momentum grows to make “personalized” medicine more “precise.” Nat. Med. 19, 249 (2013). https://doi.org/10.1038/nm0313-249

    Article  Google Scholar 

  4. Mirnezami, R., Nicholson, J., Darzi, A.: Preparing for precision medicine. N. Engl. J. Med. 366, 489–491 (2012). https://doi.org/10.1056/NEJMp1114866

    Article  Google Scholar 

  5. Chute, C.G., Ullman-Cullere, M., Wood, G.M., Lin, S.M., He, M., Pathak, J.: Some experiences and opportunities for big data in translational research. Genet. Med. Off. J. Am. Coll. Med. Genet. 15, 802–809 (2013). https://doi.org/10.1038/gim.2013.121

    Article  Google Scholar 

  6. O’Driscoll, A., Daugelaite, J., Sleator, R.D.: “Big data”, Hadoop and cloud computing in genomics. J. Biomed. Inform. 46, 774–781 (2013). https://doi.org/10.1016/j.jbi.2013.07.001

    Article  MATH  Google Scholar 

  7. Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. Proj. Hope. 33, 1123–1131 (2014). https://doi.org/10.1377/hlthaff.2014.0041

    Article  Google Scholar 

  8. Lim, J.-H., et al.: Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection. Korean J. Intern. Med. (2022). https://doi.org/10.3904/kjim.2021.518

    Article  Google Scholar 

  9. Karczewski, K.J., Snyder, M.P.: Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018). https://doi.org/10.1038/nrg.2018.4

    Article  Google Scholar 

  10. Legati, A., Giacopuzzi, E., Spinazzi, M., Lek, M.: Editorial: Application of omics approaches to the diagnosis of genetic neurological disorders. Front. Neurol. 12, 712010 (2021). https://doi.org/10.3389/fneur.2021.712010

    Article  Google Scholar 

  11. Gjoneska, E., et al.: Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015). https://doi.org/10.1038/nature14252

    Article  Google Scholar 

  12. Borad, M.J., et al.: Integrated genomic characterization reveals novel, therapeutically relevant drug targets in FGFR and EGFR pathways in sporadic intrahepatic cholangiocarcinoma. PLoS Genet. 10, e1004135 (2014). https://doi.org/10.1371/journal.pgen.1004135

    Article  Google Scholar 

  13. Häyrinen, K., Saranto, K., Nykänen, P.: Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int. J. Med. Inf. 77, 291–304 (2008). https://doi.org/10.1016/j.ijmedinf.2007.09.001

    Article  Google Scholar 

  14. Wu, P.-Y., Cheng, C.-W., Kaddi, C.D., Venugopalan, J., Hoffman, R., Wang, M.D.: Omic and electronic health record big data analytics for precision medicine. IEEE Trans. Biomed. Eng. 64, 263–273 (2017). https://doi.org/10.1109/TBME.2016.2573285

    Article  Google Scholar 

  15. Chen, E.S., Sarkar, I.N.: Mining the electronic health record for disease knowledge. In: Kumar, V.D., Tipney, H.J. (eds.) Biomedical Literature Mining. MMB, vol. 1159, pp. 269–286. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0709-0_15

    Chapter  Google Scholar 

  16. Peters, S.G., Buntrock, J.D.: Big data and the electronic health record. J. Ambulatory Care Manage. 37, 206–210 (2014). https://doi.org/10.1097/JAC.0000000000000037

    Article  Google Scholar 

  17. Kho, A.N., et al.: Practical challenges in integrating genomic data into the electronic health record. Genet. Med. Off. J. Am. Coll. Med. Genet. 15, 772–778 (2013). https://doi.org/10.1038/gim.2013.131

    Article  Google Scholar 

  18. Multi-omics approaches to disease – PubMed. https://pubmed.ncbi.nlm.nih.gov/28476144/

  19. Ritchie, M.D., Holzinger, E.R., Li, R., Pendergrass, S.A., Kim, D.: Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 85–97 (2015). https://doi.org/10.1038/nrg3868

    Article  Google Scholar 

  20. Robinson, D.R., et al.: Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017). https://doi.org/10.1038/nature23306

    Article  Google Scholar 

  21. Brandão, M., Pondé, N., Piccart-Gebhart, M.: MammaprintTM: A comprehensive review. Future Oncol. Lond. Engl. 15, 207–224 (2019). https://doi.org/10.2217/fon-2018-0221

    Article  Google Scholar 

  22. Lichtenstein, P., et al.: Environmental and heritable factors in the causation of cancer–analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78–85 (2000). https://doi.org/10.1056/NEJM200007133430201

    Article  Google Scholar 

  23. Najafi, A., Emami, N., Samad-Soltani, T.: Integration of genomics data and electronic health records toward personalized medicine: A targeted review. Front. Health Inform. 10, 86 (2021). https://doi.org/10.30699/fhi.v10i1.299

  24. Postmus, I., et al.: Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nat. Commun. 5, 5068 (2014). https://doi.org/10.1038/ncomms6068

    Article  Google Scholar 

  25. SQL - The Complete Reference.pdf. https://inspirit.net.in/books/database/SQL%20-%20The%20Complete%20Reference.pdf

  26. SQL : The Complete Reference, Second Edition: The Complete Reference, Second Edition. Groff, J.R., Weinberg, P.N. 9780072225594, Mcgraw-hill (2002). https://books.google.com.mx/books?id=OgkmmR7-XxUC

  27. The web framework for perfectionists with deadlines | Django. https://www.djangoproject.com/

  28. Python Web Development with Django - Forcier, Bissex, Chun - Addison-Wesley (2009).pdf, https://theswissbay.ch/pdf/Gentoomen%20Library/The%20Actually%20Useful%20Programming%20Library/Django/Python%20Web%20Development%20with%20Django%20-%20Forcier%2C%20Bissex%2C%20Chun%20-%20Addison-Wesley%20%282009%29/Python%20Web%20Development%20with%20Django%20-%20Forcier%2C%20Bissex%2C%20Chun%20-%20Addison-Wesley%20%282009%29.pdf

  29. Oswald, A.: Dart. Faber & Faber, London (2002)

    Google Scholar 

  30. seqTools.pdf, https://www.bioconductor.org/packages/devel/bioc/manuals/seqTools/man/seqTools.pdf

  31. Liao, Y., Smyth, G.K., Shi, W.: The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 47, e47 (2019). https://doi.org/10.1093/nar/gkz114

    Article  Google Scholar 

  32. Blumenthal, D.: Launching HITECH. N. Engl. J. Med. 362, 382–385 (2010). https://doi.org/10.1056/NEJMp0912825

    Article  Google Scholar 

  33. Hoffman, M.A.: The genome-enabled electronic medical record. J. Biomed. Inform. 40, 44–46 (2007). https://doi.org/10.1016/j.jbi.2006.02.010

    Article  Google Scholar 

  34. Warner, J.L., Jain, S.K., Levy, M.A.: Integrating cancer genomic data into electronic health records. Genome Med. 8, 113 (2016). https://doi.org/10.1186/s13073-016-0371-3

    Article  Google Scholar 

  35. Kim, D., Kim, J.H., Moore, J.H.: Translational bioinformatics: Integrating electronic health record and omics data. Pac. Symp. Biocomput. Pac. Symp. Biocomput. 26, 356–359 (2021)

    Google Scholar 

  36. Tenenbaum, J.D.: Translational bioinformatics: Past, present, and future. Genom. Proteom. Bioinform. 14, 31–41 (2016). https://doi.org/10.1016/j.gpb.2016.01.003

    Article  Google Scholar 

  37. Sethi, P., Theodos, K.: Translational bioinformatics and healthcare informatics: computational and ethical challenges. Perspect. Health Inf. Manag. 6, 1h (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Genomics, Bioinformatics, and Digital Health, Mohammed VI University of Health Sciences, 82403, Casablanca, Morocco

    Nihal Habib, Abdellah Idrissi Azami, Douae El Ghoubali, Zainab El Ouafi, Najib Al Idrissi & Hassan Ghazal

  2. Mohammed VI Center for Research and Innovation, Rabat, Morocco

    Nihal Habib, Abdellah Idrissi Azami, Douae El Ghoubali, Zainab El Ouafi, Najib Al Idrissi & Hassan Ghazal

  3. High Technology School in Morocco, 10090, Rabat, Morocco

    Mustapha Lemsayah

  4. Laboratory of Engineering Sciences, Mohamed Ben Abdellah University, 30000, Fez, Morocco

    Abdesselam Bougdira

  5. Department of Surgery, Faculty of Medicine, Mohammed VI University of Health Sciences, 82403, Casablanca, Morocco

    Najib Al Idrissi

  6. Ecole Nationale Supérieure d’Arts Et Métiers ENSAM, 10100, Rabat, Morocco

    Wajih Rhalem

  7. Moroccan Society of Digital Health, Rabat, Morocco

    Wajih Rhalem

  8. Department of Computer Sciences, Faculty of Sciences and Techniques of Tangier, 90000, Tangier, Morocco

    Mostafa Ezziyyani

  9. Department of Epidemiology and Biostatistics, International School of Public Health, Mohammed VI University of Health Sciences, 82403, Casablanca, Morocco

    Chakib Nejjari

  10. Department of Epidemiology and Public Health, Faculty of Medicine, University Sidi Mohammed Ben Abdellah, Fez, Morocco

    Chakib Nejjari

  11. National Center for Scientific and Technical Research, 10102, Rabat, Morocco

    Hassan Ghazal

Authors
  1. Nihal Habib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdellah Idrissi Azami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Douae El Ghoubali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zainab El Ouafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mustapha Lemsayah
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abdesselam Bougdira
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Najib Al Idrissi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wajih Rhalem
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mostafa Ezziyyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chakib Nejjari
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hassan Ghazal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hassan Ghazal .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Ethics declarations

Conflict of Interest

We have no conflicts of interest to disclose.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Habib, N. et al. (2023). HER-Omics, a Model of Transcriptomics Data Integration in EHRs. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_31

Download citation

Publish with us

Policies and ethics

Search

Navigation