Computational Epigenetics in Lung Cancer
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F19%3A43894516" target="_blank" >RIV/44555601:13440/19:43894516 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/B978-0-12-814513-5.00023-4" target="_blank" >http://dx.doi.org/10.1016/B978-0-12-814513-5.00023-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/B978-0-12-814513-5.00023-4" target="_blank" >10.1016/B978-0-12-814513-5.00023-4</a>
Alternative languages
Result language
angličtina
Original language name
Computational Epigenetics in Lung Cancer
Original language description
This chapter introduces the technology of gene expression profiles preprocessing based on the complex use of bicluster analysis and objective clustering inductive technology with the use of self-organizing SOTA and density DBSCAN clustering algorithms. Implementation of this technology allows us to increase the quality of epigenetics investigation in lung cancer based on the use of gene regulatory network. Inductive methods of complex system analysis were used as the basis to implement the objective clustering inductive technology of gene expression profiles. To estimate the clustering quality for equal power subsets (including the same quantity of pairwise similar objects) the complex multiplicative criterion was calculated as a combination of Calinski-Harabasz and WB index criteria. External clustering quality criteria were calculated as a normalized difference of internal clustering quality criteria for equal power subsets. Final decision concerning the determination of optimal parameters of clustering algorithm operation has been done based on the maximum value of Harrington desirability function that takes into account both the character of the objects and clusters distribution in various clustering and the differences between clustering, which are implemented on equal power data subsets. To estimate the effectiveness of the proposed technology, the data set of lung cancer patients were used. This data set includes the gene expression profiles of 96 patients, 10 of which were healthy, and 86 patients were divided according to the degree of disease severity into three groups (well, moderate, poor). The results of the simulation allow us to propose the hybrid model of step-by-step process of gene expression profiles, whereby grouping is based on the complex use of clustering and biclustering algorithms.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Computational Epigenetics and Diseases
ISBN
978-0-12-814513-5
Number of pages of the result
23
Pages from-to
397-418
Number of pages of the book
430
Publisher name
Academic Press
Place of publication
London
UT code for WoS chapter
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