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Methods Inf Med 2005; 44(01): 38-43
DOI: 10.1055/s-0038-1633921
Original Article
Schattauer GmbH

Applying Informatics in Tissue Engineering

Jie Xu
1   Department ofGeneral Surgery, The Shanghai Tenth People’s Hospital of Tongji University, Shanghai, P. R. China
,
Xiaolin Zhou
2   Department of Neurology, The Shanghai First People’s Hospital of Shanghai Jiao Tong University, Shanghai, P. R. China
,
Daping Yang
3   Department of Orthopaedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P. R. China
,
Haiyan Ge
1   Department ofGeneral Surgery, The Shanghai Tenth People’s Hospital of Tongji University, Shanghai, P. R. China
,
Qi Wang
4   Department of Bioinformatics, Harbin Medical University, Harbin, Heilongjiang, P. R. China
,
Kang Tu
4   Department of Bioinformatics, Harbin Medical University, Harbin, Heilongjiang, P. R. China
,
Tiefang Guo
3   Department of Orthopaedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P. R. China
› Author Affiliations
Further Information

Publication History

Received: 29 October 2003

accepted: 19 September 2004

Publication Date:
06 February 2018 (online)

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Summary

Objective: To facilitate tissue engineering strategies determination with informatics tools.

Methods: Firstly, tissue engineering experimental data were standardized and integrated into a centralized database; secondly, we used data mining tools (e.g. artificial neural networks and decision trees) to predict the outcomes of tissue engineering strategies; thirdly, a strategy design algorithm was developed, and its efficacy was validated with animal experiments; lastly, we constructed an online database and a decision support system for tissue engineering.

Results: The artificial neural networks and the decision trees respectively predicted the outcomes of tissue engineering strategies with the predictive accuracy of 95.14% and 85.26%. Following the strategies generated by computer, we cured 18 of the 20 experimental animals with a significantly lower cost than usual.

Conclusion: Informatics is beneficial for realizing safe, effective and economical tissue engineering.

Keywords

Tissue engineering - informatics - database - artificial intelligence - machine learning
 

  • References

  • 1 Lanza R, Vacanti JP, Langer R. editors. Principles of Tissue Engineering Second Edition. Academic Press; 1997
  • 2 Maguire JK, Cosca MF, Lynch MH. editors Clin Orthop. 1987; 216: 213-23.
    PubMedGoogle Scholar
  • 3 Langer RS, Vacanti JP. Tissue engineering: the challenges ahead. Sci Am 1999; 280 (04) 86-9.
    PubMedGoogle Scholar
  • 4 Chapekar MS. Tissue engineering: challenges and opportunities. J Biomed Mater Res 2000; 53 (06) 617-20.
    CrossrefPubMedGoogle Scholar
  • 5 Rawe J. “What will be the ten hottest jobs?” Time. 2000; 155: 54.
    PubMedGoogle Scholar
  • 6 Yang ZM, Spector M. editors Tissue Engineering. China: Chemical Industry Press; 2002
    Google Scholar
  • 7 Mason C. Automated tissue engineering: a major paradigm shift in health care. Med Device Technol 2003; 14 (01) 16-8.
    PubMedGoogle Scholar
  • 8 Martinez WL, Martinez AR. editors. Computational Statistics Handbook with MATLAB. Chapman &: Hall/CRC Press; 2001
  • 9 Robert J. editors Schalkoff Artificial Neural Networks. Addison-Wesley Press; 1997
    Google Scholar
  • 10 Bose NK, Liang P. editors Neural Network Fundamentals with Graphs, Algorithms, and Applications. McGraw-Hill Press; 1996
    Google Scholar
  • 11 Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346: 1135-8.
    CrossrefPubMedGoogle Scholar
  • 12 Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press; 1986: 45-7.
    Google Scholar
  • 13 Swain P, Hauska H. The decision tree classifier design and potential. IEEE Trans Geosci Electron 1977; 15: 142-7.
    CrossrefPubMedGoogle Scholar
  • 14 Quinlan JR. Induction of decision trees. Machine Learning 1986; 1: 81-106.
    PubMedGoogle Scholar
  • 15 Breiman L, Friedman JH, Olshen RA. editors Classification and Regression Trees. Chapman & Hall; 1984
  • 16 McKenzie DP, McGorry PD, Wallace CS. et al. Constructing a minimal diagnostic decision tree. Methods Inf Med 1993; 32: 161-6.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 17 Concannon MJ, Boschert MT, Puckett CL. Bone induction using demineralized bone in the rabbit femur: a long term study. Plast Reconstr Surg 1997; 99: 1983-8.
    CrossrefPubMedGoogle Scholar
  • 18 Haravu LJ. Web-enabled databases: an overview of technology and problems. Information Today and Tomorrow 2000; 19 (01) 7-10.
    PubMedGoogle Scholar
  • 19 William H, Mary C. Web-based access to locally developed databases. Library Computing 1999; 18 (01) 51-8.
    PubMedGoogle Scholar
  • 20 Homer A, Sussman D. B. editors Active Server Pages 3.0. Wrox Press; 1999
    Google Scholar
  • 21 http://www.mathworks.com/products/webserver/.
    PubMed
  • 22 http://msdn.microsoft.com/asp/.
    PubMed
  • 23 McQuatt A, Sleeman D, Andrews PJ. et al. Discussing anomalous situations using decision trees: a head injury case study. Methods Inf Med 2001; 40 (05) 373-9.
    Article in Thieme ConnectPubMedGoogle Scholar