Disease biomarker identification based on sample network optimization.
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| Title: | Disease biomarker identification based on sample network optimization. |
|---|---|
| Authors: | Wei PJ; Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601 Hefei, Anhui, China., Ma W; Key Laboratory of Intelligent Computing and Signal Processing, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601 Hefei, China., Li Y; Department of Cardiology, The Third Hospital of Xingtai, Xingtai 054000, Hebei, China., Su Y; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, 230088 Hefei, China; School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601 Hefei, China. Electronic address: suyansen@ahu.edu.cn. |
| Source: | Methods (San Diego, Calif.) [Methods] 2023 May; Vol. 213, pp. 42-49. Date of Electronic Publication: 2023 Mar 29. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Language: | English |
| Journal Info: | Publisher: Academic Press Country of Publication: United States NLM ID: 9426302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9130 (Electronic) Linking ISSN: 10462023 NLM ISO Abbreviation: Methods Subsets: MEDLINE |
| Imprint Name(s): | Publication: Duluth, MN : Academic Press Original Publication: San Diego : Academic Press, c1990- |
| MeSH Terms: | Algorithms*, Biomarkers |
| Abstract: | A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods. (Copyright © 2023 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Disease biomarker identification; Multi-objective evolution algorithm; Sample classification; Sample similarity network |
| Substance Nomenclature: | 0 (Biomarkers) |
| Entry Date(s): | Date Created: 20230331 Date Completed: 20230414 Latest Revision: 20230419 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.ymeth.2023.03.005 |
| PMID: | 37001685 |
| Database: | MEDLINE |
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