Comparison Analysis of Gene Expression Profiles Proximity Metrics
The problems of gene regulatory network (GRN) reconstruction and the creation of disease diagnostic effective systems based on genes expression data are some of the current directions of modern bioinformatics. In this manuscript, we present the results of the research focused on the evaluation of th...
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MDPI AG
2021-09-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/10/1812 |
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| author | Sergii Babichev Lyudmyla Yasinska-Damri Igor Liakh Bohdan Durnyak |
| author_facet | Sergii Babichev Lyudmyla Yasinska-Damri Igor Liakh Bohdan Durnyak |
| author_sort | Sergii Babichev |
| collection | DOAJ |
| description | The problems of gene regulatory network (GRN) reconstruction and the creation of disease diagnostic effective systems based on genes expression data are some of the current directions of modern bioinformatics. In this manuscript, we present the results of the research focused on the evaluation of the effectiveness of the most used metrics to estimate the gene expression profiles’ proximity, which can be used to extract the groups of informative gene expression profiles while taking into account the states of the investigated samples. Symmetry is very important in the field of both genes’ and/or proteins’ interaction since it undergirds essentially all interactions between molecular components in the GRN and extraction of gene expression profiles, which allows us to identify how the investigated biological objects (disease, state of patients, etc.) contribute to the further reconstruction of GRN in terms of both the symmetry and understanding the mechanism of molecular element interaction in a biological organism. Within the framework of our research, we have investigated the following metrics: Mutual information maximization (MIM) using various methods of Shannon entropy calculation, Pearson’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> test and correlation distance. The accuracy of the investigated samples classification was used as the main quality criterion to evaluate the appropriate metric effectiveness. The random forest classifier (RF) was used during the simulation process. The research results have shown that results of the use of various methods of Shannon entropy within the framework of the MIM metric disagree with each other. As a result, we have proposed the modified mutual information maximization (MMIM) proximity metric based on the joint use of various methods of Shannon entropy calculation and the Harrington desirability function. The results of the simulation have also shown that the correlation proximity metric is less effective in comparison to both the MMIM metric and Pearson’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> test. Finally, we propose the hybrid proximity metric (HPM) that considers both the MMIM metric and Pearson’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> test. The proposed metric was investigated within the framework of one-cluster structure effectiveness evaluation. To our mind, the main benefit of the proposed HPM is in increasing the objectivity of mutually similar gene expression profiles extraction due to the joint use of the various effective proximity metrics that can contradict with each other when they are used alone. |
| first_indexed | 2024-03-10T06:10:26Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T06:10:26Z |
| publishDate | 2021-09-01 |
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| spelling | doaj.art-6858910598be4c57bde23518fb3268702023-11-22T20:09:27ZengMDPI AGSymmetry2073-89942021-09-011310181210.3390/sym13101812Comparison Analysis of Gene Expression Profiles Proximity MetricsSergii Babichev0Lyudmyla Yasinska-Damri1Igor Liakh2Bohdan Durnyak3Department of Physics, Kherson State University, 73000 Kherson, UkraineDepartment of Computer Science and Information Technology, Ukrainian Academy of Printing, 79000 Lviv, UkraineDepartment of Informatics, Phisical and Mathematical Disciplines, Uzhhorod National University, 88000 Uzhhorod, UkraineDepartment of Computer Science and Information Technology, Ukrainian Academy of Printing, 79000 Lviv, UkraineThe problems of gene regulatory network (GRN) reconstruction and the creation of disease diagnostic effective systems based on genes expression data are some of the current directions of modern bioinformatics. In this manuscript, we present the results of the research focused on the evaluation of the effectiveness of the most used metrics to estimate the gene expression profiles’ proximity, which can be used to extract the groups of informative gene expression profiles while taking into account the states of the investigated samples. Symmetry is very important in the field of both genes’ and/or proteins’ interaction since it undergirds essentially all interactions between molecular components in the GRN and extraction of gene expression profiles, which allows us to identify how the investigated biological objects (disease, state of patients, etc.) contribute to the further reconstruction of GRN in terms of both the symmetry and understanding the mechanism of molecular element interaction in a biological organism. Within the framework of our research, we have investigated the following metrics: Mutual information maximization (MIM) using various methods of Shannon entropy calculation, Pearson’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> test and correlation distance. The accuracy of the investigated samples classification was used as the main quality criterion to evaluate the appropriate metric effectiveness. The random forest classifier (RF) was used during the simulation process. The research results have shown that results of the use of various methods of Shannon entropy within the framework of the MIM metric disagree with each other. As a result, we have proposed the modified mutual information maximization (MMIM) proximity metric based on the joint use of various methods of Shannon entropy calculation and the Harrington desirability function. The results of the simulation have also shown that the correlation proximity metric is less effective in comparison to both the MMIM metric and Pearson’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> test. Finally, we propose the hybrid proximity metric (HPM) that considers both the MMIM metric and Pearson’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> test. The proposed metric was investigated within the framework of one-cluster structure effectiveness evaluation. To our mind, the main benefit of the proposed HPM is in increasing the objectivity of mutually similar gene expression profiles extraction due to the joint use of the various effective proximity metrics that can contradict with each other when they are used alone.https://www.mdpi.com/2073-8994/13/10/1812symmetry of molecular elements interactionsgene expression profilesmutual information maximization criterioncorrelation distancePearson’s <math display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math> testHarrington desirability index |
| spellingShingle | Sergii Babichev Lyudmyla Yasinska-Damri Igor Liakh Bohdan Durnyak Comparison Analysis of Gene Expression Profiles Proximity Metrics Symmetry symmetry of molecular elements interactions gene expression profiles mutual information maximization criterion correlation distance Pearson’s <math display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math> test Harrington desirability index |
| title | Comparison Analysis of Gene Expression Profiles Proximity Metrics |
| title_full | Comparison Analysis of Gene Expression Profiles Proximity Metrics |
| title_fullStr | Comparison Analysis of Gene Expression Profiles Proximity Metrics |
| title_full_unstemmed | Comparison Analysis of Gene Expression Profiles Proximity Metrics |
| title_short | Comparison Analysis of Gene Expression Profiles Proximity Metrics |
| title_sort | comparison analysis of gene expression profiles proximity metrics |
| topic | symmetry of molecular elements interactions gene expression profiles mutual information maximization criterion correlation distance Pearson’s <math display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math> test Harrington desirability index |
| url | https://www.mdpi.com/2073-8994/13/10/1812 |
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