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
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 da
ta is analyzed using JSM and AQ methodology.
(More)