Assessment of Dendritic Cell Therapy Effectiveness Based on the Feature Extraction from Scientific Publications

Alexey Yu. Lupatov, Alexander I. Panov, Roman E. Suvorov, Alexander V. Shvets, Konstantin N. Yarygin, Galina D. Volkova

2015

Abstract

Dendritic cells (DCs) vaccination is a promising way to contend cancer metastases especially in the case of immunogenic tumors. Unfortunately, it is only rarely possible to achieve a satisfactory clinical outcome in the majority of patients treated with a particular DC vaccine. Apparently, DC vaccination can be successful with certain combinations of features of the tumor and patients immune system that are not yet fully revealed. Difficulty in predicting the results of the therapy and high price of preparation of individual vaccines prevent wider use of DC vaccines in medical practice. Here we propose an approach aimed to uncover correlation between the effectiveness of specific DC vaccine types and personal characteristics of patients to increase efficiency of cancer treatment and reduce prices. To accomplish this, we suggest two-step analysis of published clinical trials results for DCs vaccines: first, the information extraction subsystem is trained, and, second, the extracted data is analyzed using JSM and AQ methodology.

References

  1. Aggarwal, C. C. and Zhai, C., editors (2012). Mining Text Data. Springer.
  2. Anshakov, O. M., Skvortcov, D. P., and Finn, V. K. (1991). On logical construction of JSM-method of automated hypotheses generation. Doklady Akademii nauk SSSR, 320(6):1331-1336.
  3. Blinova, V. G., Dobrynin, D. A., Finn, V. K., Kuznetsov, S. O., and Pankratova, E. S. (2003). Toxicology analysis by means of the JSM-method. Bioinformatics, 19(10):1201-1207.
  4. Cauwenberghs, G. and Poggio, T. (2000). Incremental and decremental support vector machine learning. In Advances in Neural Information Processing Systems, volume 13.
  5. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa, P. (2011). Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493-2537.
  6. Draube, A., Klein-González, N., Mattheus, S., Brillant, C., Hellmich, M., Engert, A., and von Bergwelt-Baildon, M. (2011). Dendritic cell based tumor vaccination in prostate and renal cell cancer: a systematic review and meta-analysis. PLoS One, 6(4):1-11.
  7. Ferrucci, D. and Lally, A. (2004). Uima: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering, 10(3-4):327-348.
  8. Figdor, C., de Vries, I., Lesterhuis, W., and Melief, C. (2004). Dendritic cell immunotherapy: mapping the way. Nat Med., 10(5):475-480.
  9. Finn, V. K., Gergely, T., and Kuznetsov, S. O. (1996). Plausible reasoning for open problem domains. In Proceedings of the 1996 IEEE International Symposium on Intelligent Control, pages 242-247, Dearborn, MI.
  10. Gaizauskas, R., Hepple, M., Davis, N., Guo, Y., Harkema, H., Roberts, A., and Roberts, I. (2003). AMBIT: Acquiring medical and biological information from text. In All Hands Meeting., pages 370-373.
  11. Goldberg, D., editor (1989). Genetic Algorithms in Search, Optimization and Machine Learning. AddisonWesley Professional.
  12. Massachusetts Institute of technology (2014). MIMIC II.
  13. Michalski, R. S. (1973). AQVAL/1-computer implementation of variable-valued logic system VL1 and examples of its application to pattern recognition. In Proc. Of the First Int. Joint Conf. on Pattern Recognition, pages 3-17, Washington, DS.
  14. Murthy, V., Moiyadi, A., Sawant, R., and Sarin, R. (2009). Clinical considerations in developing dendritic cell vaccine based immunotherapy protocols in cancer. Curr Mol Med., 9(6):725-731.
  15. Nurminen, A. (2013). Algorithmic extraction of data in tables in pdf documents. Master's thesis, Tampere University of Technology.
  16. Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., and Chute, C. G. (2010). Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association, 17(5):507-513.
  17. Sergienko, R. and Semenkin, E. (2013). Michigan and pittsburgh methods combination for fuzzy classifier design with coevolutionary algorithm. In Proc. of 2013 IEEE Congress on Evolutionary Computation, pages 3252- 3259, Cancun, Mexico.
  18. Shortman, K. and Caux, C. (1997). Dendritic cell development: multiple pathways to nature's adjuvants. Stem Cells, 15:409-419.
  19. Wei, Y., Sticca, R., Holmes, L., Burgin, K., Li, J., Williamson, J., Evans, L., Smith, S., Stephenson, J., and Wagner, T. (2006). Dendritoma vaccination combined with low dose interleukin-2 in metastatic melanoma patients induced immunological and clinical responses. Int J Oncol., 28(3):585-593.
  20. Welzen-Coppens, J., van Helden-Meeuwsen, C., Drexhage, H., and Versnel, M. (2012). Abnormalities of dendritic cell precursors in the pancreas of the nod mouse model of diabetes. Eur J Immunol., 42(1):186-194.
  21. Wojtusiak, J., Michalski, R. S., Kaufman, K. A., and Pietrzykowski, J. (2006). The AQ21 natural induction program for pattern discovery: Initial version and its novel features. In 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), pages 523-526, Arlington, Virginia.
  22. Yannelli, J. R. and Wroblewski, J. M. (2004). On the road to a tumor cell vaccine: 20 years of cellular immunotherapy. Vaccine, 23:97-113.
  23. Zhou, L. and Tedder, T. (1996). CD14+ blood monocytes can differentiate into functionally mature CD83+ dendritic cells. Proc. Natl. Acad. Sci. USA, 93:2588- 2592.
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Paper Citation


in Harvard Style

Yu. Lupatov A., I. Panov A., E. Suvorov R., V. Shvets A., N. Yarygin K. and D. Volkova G. (2015). Assessment of Dendritic Cell Therapy Effectiveness Based on the Feature Extraction from Scientific Publications . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 270-276. DOI: 10.5220/0005248802700276


in Bibtex Style

@conference{icpram15,
author={Alexey Yu. Lupatov and Alexander I. Panov and Roman E. Suvorov and Alexander V. Shvets and Konstantin N. Yarygin and Galina D. Volkova},
title={Assessment of Dendritic Cell Therapy Effectiveness Based on the Feature Extraction from Scientific Publications},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={270-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005248802700276},
isbn={978-989-758-077-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Assessment of Dendritic Cell Therapy Effectiveness Based on the Feature Extraction from Scientific Publications
SN - 978-989-758-077-2
AU - Yu. Lupatov A.
AU - I. Panov A.
AU - E. Suvorov R.
AU - V. Shvets A.
AU - N. Yarygin K.
AU - D. Volkova G.
PY - 2015
SP - 270
EP - 276
DO - 10.5220/0005248802700276