Heterogeneous network-based algorithms in the biomedical data mining: A review from technical perspective
Background: Heterogeneous network-based methods are powerful analytical tools for many real-world data mining tasks in biomedical field. The specific aim of this survey is to examine the representative algorithms used in heterogeneous network data mining tasks and concentrate on biomedical domain to...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-09-01
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| Series: | Informatics and Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949953424000158 |
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| Summary: | Background: Heterogeneous network-based methods are powerful analytical tools for many real-world data mining tasks in biomedical field. The specific aim of this survey is to examine the representative algorithms used in heterogeneous network data mining tasks and concentrate on biomedical domain to analyze the application of these techniques in the real world. Methods: This study is a review. In this study, keywords of heterogeneous network-based algorithms were used to search in CNKI and Web of Science databases, and the results were manually analyzed. Among these results, 100 key papers most relevant to heterogeneous network-based algorithms in the biomedical data mining were selected for review. Through the review of the research literature, we first introduce the basic concepts and some challenges in this field; then we provide two taxonomies of existing heterogeneous network representation learning algorithms from technical and feature perspectives; meanwhile, we also systemically summarize research developments of heterogeneous network generation algorithms. In addition, we further present major data mining tasks in the real-world application of biomedical domain. Finally, we explore the advanced topics and forecast the future research directions of heterogeneous networks. Findings: The heterogeneous network-based algorithms are analyzed from technical perspective. The detailed analysis of these algorithms contributes to a deeper understanding of their features and applicability, and promotes their use in data mining tasks. The analysis of the application of these algorithms in biomedical research help advance biomedical research from the molecular level to the healthcare system. Deep learning frameworks are the current focus of these algorithms. Interpretation: This survey helps the understanding of heterogeneous network algorithms and envisions to provide a universal reference and guideline for heterogeneous network data mining tasks in the field of biomedicine. |
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| ISSN: | 2949-9534 |