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On the Methods of Artificial Intelligence for Analysis of Oncological Data

On the Methods of Artificial Intelligence for Analysis of Oncological Data A brief overview of artificial intelligence techniques applied to medical data related to oncology is provided. The actual goals of using artificial intelligence are listed, that is, the types of applied problems solved with its use. The initial information is described, which, as a rule, contains genotypic data: about DNA and associated molecules, as well as the general clinical parameters of patients. The description of the logical-mathematical and software approaches of the most known solutions in this area is given. This work is intended to familiarize data analysts with the challenges in modern oncology with the use of artificial intelligence, as well as to guide biomedical researchers on the variety of data-mining methods and capabilities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Documentation and Mathematical Linguistics Springer Journals

On the Methods of Artificial Intelligence for Analysis of Oncological Data

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2020. ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2020, Vol. 54, No. 5, pp. 255–259. © Allerton Press, Inc., 2020. Russian Text © The Author(s), 2020, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2020, No. 9, pp. 21–26.
ISSN
0005-1055
eISSN
1934-8371
DOI
10.3103/S0005105520050027
Publisher site
See Article on Publisher Site

Abstract

A brief overview of artificial intelligence techniques applied to medical data related to oncology is provided. The actual goals of using artificial intelligence are listed, that is, the types of applied problems solved with its use. The initial information is described, which, as a rule, contains genotypic data: about DNA and associated molecules, as well as the general clinical parameters of patients. The description of the logical-mathematical and software approaches of the most known solutions in this area is given. This work is intended to familiarize data analysts with the challenges in modern oncology with the use of artificial intelligence, as well as to guide biomedical researchers on the variety of data-mining methods and capabilities.

Journal

Automatic Documentation and Mathematical LinguisticsSpringer Journals

Published: Dec 11, 2020

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