Browsing by Author "Babichev, S."

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    APPLICATION OF DATA MINING AND MACHINE LEARNING METHODS TO DEVELOP A DISEASE DIAGNOSIS SYSTEM BASED ON GENE EXPRESSION DATA
    (2024) Babichev, S.; Liakh, I.; Durnyak, B.; Бабічев, С. А.
    This monograph addresses the pressing scientific and practical problem of devel- oping and applying methodological foundations of information technology for pro- cessing gene expression data. This technology integrates gene ontology analysis, cluster-bicluster analysis, and deep learning methods for solving tasks in the field of bioinformatics. Its distinctive feature is higher adequacy in disease diagnosis compared to existing methods, achieved through hybridising existing methods and algorithms for big data processing, optimization of model hyperparameters using quantitative quality criteria, and considering the type of data being studied. The relevance of the research topic is underscored by the current absence of adequate in- formation technology for processing gene expression data to identify significant and mutually correlated genes capable of diagnosing the diseases with high confidence and predicting their further development at the genetic level by modelling changes in the expression of target genes and their impact on the studied object. The ef- ficiency of the diagnostic process can be enhanced through model hybridization, which involves the comprehensive application of various methods and algorithms to improve the reliability of decision-making at the corresponding stage. This ap- proach necessitates the development of hybrid quality criteria for evaluating the outcome at each stage. Another way to enhance the effectiveness of gene expression data processing technology is the use of method ensembles, followed by compar- ing the results obtained by each method using appropriate quality criteria and calculating a comprehensive criterion for making the final decision on the model structure. The monograph can be interested for scientists specialized in the fields of both development and applying data science techniques in various fields of scientific research.
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    METHODS, MODELS AND INFORMATION TECHNOLOGY OF COMPLEX DATA PROCESSING IN THE FIELDS OF TECHNICAL DIAGNOSTICS AND BIOINFORMATICS
    (2020) Babichev, S.; Durnyak, B.; Бабічев, С. А.
    This monograph reflects the results of the authors’ research concerning development and applying the data science techniques in the fields of technical diagnostic and bioinformatics. The noised signals filtering is very important part of data pre- processing techniques. The authors solve this task based on complex use of Huang transform and wavelet analysis techniques. Another direction of authors’ research is devoted to one of current directions of modern bioinformatics: development of techniques of gene expression profiles pro- cessing for purpose of gene regulatory networks reconstruction and validation of the reconstructed models. The monograph presents the authors’ solutions concerning: gene expression array formation using Bioconductor package of R software; non- informative gene expression profiles reducing based on the complex use of fuzzy inference system and clustering quality criteria; stepwise cluster-bicluster analysis of gene expression profiles; reconstruction of gene regulatory networks using corre- lation and ARACNE inference algorithms based on Cytoscape software; validation of the reconstructed models using ROC analysis theory. The monograph can be interested for scientists specialized in the fields of both development and applying data science techniques in various fields of scientific research.