Skip to main content
SpringerLink
Log in

Lecture 5 Semi-structured, Weakly Structured, and Unstructured Data

  • Chapter
  • First Online:
Biomedical Informatics

  • 2297 Accesses

Abstract

At the end of this fourth lecture, you:

  • would have acquired background knowledge on some issues in standardization and structurization of data;

  • would have a general understanding of modeling knowledge in medicine and biomedical informatics;

  • would get some basic knowledge on medical Ontologies and be aware of the limits, restrictions, and shortcomings of them;

  • would know the basic ideas and the history of the International Classification of Diseases (ICD);

  • would have a view on the Standardized Nomenclature of Medicine Clinical Terms (SNOMED CT);

  • would have some basic knowledge on Medical Subject Headings (MeSH);

  • would be able to understand the fundamentals and principles of the Unified Medical Language System (UMLS).

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    NP = nondeterministic polynomial time; in computational complexity theory NP is one of the fundamental complexity classes.

References

  • Aarts E, Lenstra J (1997) Local search in combinatorial optimization. Wiley, New York, NY

    MATH  Google Scholar 

  • Achard F, Vaysseix G, Barillot E (2001) XML, bioinformatics and data integration. Bioinformatics 17(2):115

    Article  Google Scholar 

  • Barabási A-L, Albert R, Jeong H (1999) Mean-field theory for scale-free random networks. Phys A Stat Mech Its Appl 272(1–2):173–187

    Article  Google Scholar 

  • Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  Google Scholar 

  • Bondy JA, Murty USR (1976) Graph theory with applications. Macmillan, London

    MATH  Google Scholar 

  • Boyen P, Van Dyck D, Neven F, Van Ham RCHJ, Van Dijk ADJ (2011) SLIDER: a generic metaheuristic for the discovery of correlated motifs in protein-protein interaction networks. IEEE/ACM Trans Comput Biol Bioinform 8(5):1344–1357

    Google Scholar 

  • Catchpoole DR, Kennedy P, Skillicorn DB, Simoff S (2010) The curse of dimensionality: a blessing to personalized medicine. J Clin Oncol 28(34):E723–E724

    Article  Google Scholar 

  • Chien KR, Domian IJ, Parker KK (2008) Cardiogenesis and the complex biology of regenerative cardiovascular medicine. Science 322(5907):1494

    Article  Google Scholar 

  • Costa LF, Rodrigues FA, Cristino AS (2008) Complex networks: the key to systems biology. Genet Mol Biol 31(3):591–601

    Article  Google Scholar 

  • Costa LF, Rodrigues FA, Travieso G, Boas PRV (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242

    Article  Google Scholar 

  • Darwin C (1859) On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. John Murray, London

    Google Scholar 

  • Dehmer M, Emmert-Streib F, Mehler A (2011) Towards an information theory of complex networks: statistical methods and applications. Birkhäuser, Boston, MA

    Book  Google Scholar 

  • Dhar V (2013) Data science and prediction. Commun ACM 56(12):64–73

    Article  Google Scholar 

  • Diestel R (2010) Graph theory, 4th edn. Springer, Berlin

    Book  Google Scholar 

  • Dorogovtsev SN, Mendes JFF (2003) Evolution of networks: from biological nets to the internet and WWW. Oxford University Press, New York, NY

    Book  Google Scholar 

  • Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York, NY

    Google Scholar 

  • Edelsbrunner H, Harer JL (2010) Computational topology: an introduction. American Mathematical Society, Providence, RI

    Google Scholar 

  • Fekete J-D (2004) The infovis toolkit. Information visualization, INFOVIS 2004. IEEE, Washington, DC, pp 167–174

    Google Scholar 

  • Forster C, Vossen G (2012) Exploiting XML technologies in medical information systems. In: Proceedings of the 25th Bled eConference eDependability: reliable and trustworthy eStructures, eProcesses, eOperations and eServices for the future, Bled, Slovenia, pp 70–83

    Google Scholar 

  • Gaal SA (1966) Point set topology, 2nd edn. Academic, New York, NY

    Google Scholar 

  • Geschwind DH, Konopka G (2009) Neuroscience in the era of functional genomics and systems biology. Nature 461(7266):908–915

    Article  Google Scholar 

  • Golumbic MC (2004) Algorithmic graph theory and perfect graphs. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Harary F (1969) Graph theory. Addison-Wesley, Reading, MA

    Google Scholar 

  • Hatcher A (2002) Algebraic topology. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Henzinger MR, Klein P, Rao S, Subramanian S (1997) Faster shortest-path algorithms for planar graphs. J Comput Syst Sci 55(1):3–23

    Article  MATH  MathSciNet  Google Scholar 

  • Hodgman CT, French A, Westhead DR (2010) Bioinformatics, 2nd edn. Taylor & Francis, New York, NY

    Google Scholar 

  • Holzinger A (2003) Basiswissen IT/Informatik. Band 2: Informatik. Das Basiswissen für die Informationsgesellschaft des 21. Jahrhunrets, Vogel Buchverlag, Wuerzburg.

    Google Scholar 

  • Holzinger A (2011) Weakly structured data in health-informatics: the challenge for human-computer interaction. In: Baghaei N, Baxter G, Dow L, Kimani S (eds) Proceedings of INTERACT 2011 workshop: promoting and supporting healthy living by design. IFIP, Lisbon, Portugal, pp 5–7

    Google Scholar 

  • Holzinger A (2012) On knowledge discovery and interactive intelligent visualization of biomedical data: challenges in human–computer interaction & biomedical informatics. In: Helfert M, Fancalanci C, Filipe J (eds) DATA - international conference on data technologies and applications. INSTICC, Rome, pp 5–16

    Google Scholar 

  • Holzinger A, Ofner B, Stocker C, Valdez AC, Schaar AK, Ziefle M, Dehmer M (2013a) On graph entropy measures for knowledge discovery from publication network data. In: Cuzzocrea A, Kittl C, Simos DE, Weippl E, Xu L (eds) Multidisciplinary research and practice for information systems, vol LNCS 8127, Springer lecture notes in computer science. Springer, Heidelberg, pp 354–362

    Google Scholar 

  • Holzinger A, Simonic KM, Geier M, Ofner B, Müller R, Heschl S, Prause G (2013) Constraints of list‐based knowledge interaction on an android app for emergency medicine. Medicine 2.0 London, Oral Presentation on 23 Sept 2013 (Online). http://www.medicine20congress.com/ocs/index.php/med/med2013/paper/view/1479

  • Holzinger A, Stocker C, Ofner B, Prohaska G, Brabenetz A, Hofmann-Wellenhof R (2013c) Combining HCI, natural language processing, and knowledge discovery - potential of IBM content analytics as an assistive technology in the biomedical domain, vol LNCS 7947, Springer lecture notes in computer science. Springer, Heidelberg, pp 13–24

    Google Scholar 

  • Kim PM, Korbel JO, Gerstein MB (2007) Positive selection at the protein network periphery: evaluation in terms of structural constraints and cellular context. Proc Natl Acad Sci U S A 104(51):20274–20279

    Article  Google Scholar 

  • Kleinberg JM (2000) Navigation in a small world. Nature 406(6798):845

    Article  Google Scholar 

  • Koontz WLG, Narendra PM, Fukunaga K (1976) A graph-theoretic approach to nonparametric cluster analysis. IEEE Trans Comput 100(9):936–944

    Article  MathSciNet  Google Scholar 

  • Kreuzthaler M, Bloice MD, Faulstich L, Simonic KM, Holzinger A (2011) A comparison of different retrieval strategies working on medical free texts. J Univ Comput Sci 17(7):1109–1133

    Google Scholar 

  • Kropatsch W, Burge M, Glantz R (2001) Graphs in image analysis. In: Kropatsch W, Bischof H (eds) Digital image analysis. Springer, New York, NY, pp 179–197

    Chapter  Google Scholar 

  • Lage K, Møllgård K, Greenway S, Wakimoto H, Gorham JM, Workman CT, Bendsen E, Hansen NT, Rigina O, Roque FS (2010) Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol Syst Biol 6(1):1–9

    Google Scholar 

  • Lézoray O, Grady L (2012) Graph theory concepts and definitions used in image processing and analysis. In: Lézoray O, Grady L (eds) Image processing and analysing with graphs: theory and practice. CRC Press, Boca Raton, FL, pp 1–24

    Google Scholar 

  • Louie B, Mork P, Martin-Sanchez F, Halevy A, Tarczy-Hornoch P (2007) Data integration and genomic medicine. J Biomed Inform 40(1):5–16

    Article  Google Scholar 

  • Lucchi A, Smith K, Achanta R, Lepetit V, Fua P (2010) A fully automated approach to segmentation of irregularly shaped cellular structures in EM images. Medical image computing and computer-assisted intervention—MICCAI 2010. Springer, Berlin, pp 463–471

    Google Scholar 

  • Meijster A, Roerdink JB (1995) A proposal for the implementation of a parallel watershed algorithm. Computer analysis of images and patterns. Springer, Berlin, pp 790–795

    Book  Google Scholar 

  • Milgram S (1967) The small world problem. Psychol Today 2(1):60–67

    MathSciNet  Google Scholar 

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  MATH  MathSciNet  Google Scholar 

  • Rassinoux A-M, Lovis C, Baud R, Geissbuhler A (2003) XML as standard for communicating in a document-based electronic patient record: a 3 years experiment. Int J Med Inform 70(2–3):109–115

    Article  Google Scholar 

  • Roerdink JB, Meijster A (2000) The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41(1):187–228

    MATH  MathSciNet  Google Scholar 

  • Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Søeby K, Bredkjær S, Juul A, Werge T, Jensen LJ, Brunak S (2011) Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 7(8):e1002141

    Article  Google Scholar 

  • Salgado H, Santos-Zavaleta A, Gama-Castro S, Peralta-Gil M, Peñaloza-Spínola MI, Martínez-Antonio A, Karp PD, Collado-Vides J (2006) The comprehensive updated regulatory network of Escherichia coli K-12. BMC Bioinform 7(1):5

    Article  Google Scholar 

  • Schadt EE, Lum PY (2006) Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J Lipid Res 47(12):2601–2613

    Article  Google Scholar 

  • Schmid AK, Reiss DJ, Pan M, Koide T, Baliga NS (2009) A single transcription factor regulates evolutionarily diverse but functionally linked metabolic pathways in response to nutrient availability. Mol Syst Biol 5(1):1–9

    Google Scholar 

  • Simon HA (1973) The structure of ill structured problems. Artif Intell 4(3–4):181–201

    Article  Google Scholar 

  • Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–276

    Article  Google Scholar 

  • Usdin T, Graham T (1998) XML: not a silver bullet, but a great pipe wrench. ACM Stand View 6(3):125–132

    Google Scholar 

  • Van Den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20(8):519–534

    Article  Google Scholar 

  • Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Machine Intell 13(6):583–598

    Article  Google Scholar 

  • Wang Z, Zhang JZ (2007) In search of the biological significance of modular structures in protein networks. PLoS Comput Biol 3(6):1011–1021

    MathSciNet  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

    Article  Google Scholar 

  • Wiltgen M, Tilz GP (2009) Homology modelling: a review about the method on hand of the diabetic antigen GAD 65 structure prediction. Wiener Medizinische Wochenschrift 159(5):112–125

    Article  Google Scholar 

  • Wittkop T, Emig D, Truss A, Albrecht M, Böcker S, Baumbach J (2011) Comprehensive cluster analysis with transitivity clustering. Nat Protoc 6(3):285–295

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical University Graz and Graz University of Technology, Graz, Austria

    Andreas Holzinger

Authors
  1. Andreas Holzinger
    View author publications

    You can also search for this author in PubMed Google Scholar

1 Supplementary Materials

Below is the link to the electronic supplementary material.

5_LV444_152_WEAKLYSTRUCTURED_DATA_HOLZINGER_2013_14_lowres.pdf (PDF 11.1 MB)

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Holzinger, A. (2014). Lecture 5 Semi-structured, Weakly Structured, and Unstructured Data. In: Biomedical Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-04528-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04528-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04527-6

  • Online ISBN: 978-3-319-04528-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation