Skip to main content
Springer Nature Link
Log in

Data analytics and visualization for inspecting cancers and genes

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper describes our latest research in data analytics and visualization for bioinformatics and healthcare. Each year many patients have suffered cancers. Analytics and visualization can help to simulate the development of malignant tumors and help identify weak spots of tumor for treatment, inspect malignant tumors in general and inspect whether genes have cancerous cells. Related literature, technologies, simulation results with explanation, performance evaluation and comparisons with other work have been discussed in details. We can process training data with a low completion time to achieve simulations of malignant tumors and genes to inspect their status, as well as the querying the output data within seconds. Our malignant tumor and gene simulation can achieve 360 degrees for an inspection of cancerous presence. We conclude that data analytics and visualization can provide effective and efficient healthcare research and also other type of interdisciplinary research.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Agostinelli S, Allison J, Amako KA, Apostolakis J, Araujo H, Arce P et al (2003) GEANT4—a simulation toolkit. Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment 506(3):250–303

    Article  Google Scholar 

  2. Boik J (2001) Natural compounds in cancer therapy. Oregon Medical Press, Princeton

  3. Bostock M, Ogievetsky V, Heer J (2011) D3 data-driven documents. IEEE Trans Vis Comput Graph 17(12):2301–2309

    Article  Google Scholar 

  4. Cao J, Cui H, Shi H, Jiao L (2016) Big data: a parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce. PLoS One 11(6):e0157551

    Article  Google Scholar 

  5. Chang V (2014) The business intelligence as a service in the cloud. Futur Gener Comput Syst 37:512–534

    Article  Google Scholar 

  6. Chang V (2017) Towards data analysis for weather cloud computing. Knowl-Based Syst 127:29–45

    Article  Google Scholar 

  7. Christopher R, Dhiman A, Fox J, Gendelman R, Haberitcher T, Kagle D et al (2004) Data-driven computer simulation of human cancer cell. Ann N Y Acad Sci 1020(1):132–153

    Article  Google Scholar 

  8. Cios KJ, Moore GW (2002) Uniqueness of medical data mining. Artif Intell Med 26(1):1–24

    Article  Google Scholar 

  9. Collett D (2015) Modelling survival data in medical research. CRC Press, Boca Raton

    Google Scholar 

  10. Cuomo MI (2012) A world without cancer: the making of a new cure and the real promise of prevention. Rodale, Emmaus

    Google Scholar 

  11. Green TM, Ribarsky W, Fisher B (2008) Visual analytics for complex concepts using a human cognition model. In: Visual Analytics Science and Technology, 2008. VAST'08. IEEE Symposium on. IEEE, p 91–98

  12. Green TM, Ribarsky W, Fisher B (2009) Building and applying a human cognition model for visual analytics. Inf Vis 8(1):1–13

    Article  Google Scholar 

  13. Hu J, Sharma S, Gao Z, Chang V (2017) Gene-based collaborative filtering using recommender system. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.04.010

  14. Huang D, Tory M, Aseniero BA, Bartram L, Bateman S, Carpendale S et al (2015) Personal visualization and personal visual analytics. IEEE Trans Vis Comput Graph 21(3):420–433

    Article  Google Scholar 

  15. Lin J, Dyer C (2010) Data-intensive text processing with MapReduce. Synth Lect Hum Lang Technol 3(1):1–177

    Article  Google Scholar 

  16. Marušić M (1996) Mathematical models of tumor growth. Mathematical. Communications 1(2):175–188

    MATH  Google Scholar 

  17. Matsunaga A, Tsugawa M, Fortes J (2008) Cloudblast: combining mapreduce and virtualization on distributed resources for bioinformatics applications. In: eScience, 2008. eScience'08. IEEE Fourth International Conference on. IEEE, p 222–229

  18. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303

    Article  Google Scholar 

  19. Pienta KJ (ed) (2012) Diagnosis and Treatment of Genitourinary Malignancies (Vol. 88). Springer Science & Business Media, New York

    Google Scholar 

  20. Priestman T (2012) Cancer chemotherapy in clinical practice. Springer Science & Business Media, New York

    Book  Google Scholar 

  21. Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, Cambridge

    Book  Google Scholar 

  22. Roose T, Chapman SJ, Maini PK (2007) Mathematical models of avascular tumor growth. SIAM Rev 49(2):179–208

    Article  MathSciNet  MATH  Google Scholar 

  23. Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11(9):647

    Article  Google Scholar 

  24. Schatz MC (2009) CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11):1363–1369

    Article  Google Scholar 

  25. Siddiqa A, Karim A, Chang V (2017) SmallClient for big data: an indexing framework towards fast data retrieval. Clust Comput 20(2):1193–1208

    Article  Google Scholar 

  26. Suresh P, Hsu SH, Reklaitis GV, Venkatasubramanian V (2010) OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 2: applications. Ind Eng Chem Res 49(17):7768–7781

    Article  Google Scholar 

  27. Venkitaraman AR (2002) Cancer susceptibility and the functions of BRCA1 and BRCA2. Cell 108(2):171–182

    Article  Google Scholar 

  28. Wilkinson DJ (2011) Stochastic modelling for systems biology. CRC Press, Boca Raton

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Victor Chang

Authors
  1. Victor Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Victor Chang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, V. Data analytics and visualization for inspecting cancers and genes. Multimed Tools Appl 77, 17693–17707 (2018). https://doi.org/10.1007/s11042-017-5186-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5186-8

Keywords

Search

Navigation