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Authors: Alexey Yu. Lupatov 1 ; Alexander I. Panov 2 ; Roman E. Suvorov 2 ; Alexander V. Shvets 2 ; Konstantin N. Yarygin 1 and Galina D. Volkova 3

Affiliations: 1 Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Russian Federation ; 2 Institute for Systems Analysis of the Russian Academy of Sciences, Russian Federation ; 3 Moscow State University of Technology "Stankin", Russian Federation

ISBN: 978-989-758-077-2

Keyword(s): Dendritic Cells, Anticancer Vaccine, Cell Therapy, Natural Language Processing, Data Mining, Text Mining, JSM-method, Genetic Algorithm, AQ-method.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Feature Selection and Extraction ; Hybrid Learning Algorithms ; Information Retrieval and Learning ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Missing Data ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems ; Theory and Methods

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 dat a is analyzed using JSM and AQ methodology. (More)

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Paper citation in several formats:
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

@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},
}

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

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