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Open AccessArticle

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

by 1,2,*,†,‡ and 1,‡
1
Department of Informatics, Faculty of Science, Jan Evangelista Purkyně University in Ústí nad Labem, 40096 Ústí nad Labem, Czech Republic
2
Department of Computer Science, Software Engineering and Economic Cybernetics, Faculty of Computer Science, Physics and Mathematics, Kherson State University, Kherson 73003, Ukraine
*
Author to whom correspondence should be addressed.
Current address: Pasteurova 3632/15, 400 96 Ústí nad Labem, Czech Republic.
The authors contributed to this work as follows: the first author—80%, the second one—20%.
Diagnostics 2020, 10(8), 584; https://doi.org/10.3390/diagnostics10080584
Received: 5 July 2020 / Revised: 10 August 2020 / Accepted: 11 August 2020 / Published: 12 August 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients’ health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient’s health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients’ gene expression profiles—the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps—in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers—logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient’s health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health. View Full-Text
Keywords: gene expression profiles; lung cancer; clustering; classification; ML-based binary classifiers; SOTA clustering algorithm; clustering quality criteria; ROC analysis; fuzzy inference system gene expression profiles; lung cancer; clustering; classification; ML-based binary classifiers; SOTA clustering algorithm; clustering quality criteria; ROC analysis; fuzzy inference system
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Babichev, S.; Škvor, J. Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods. Diagnostics 2020, 10, 584.

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