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Abstract
Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine’s performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs.
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Authors (5)
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Maiqi Wang
- Chengdu University, China
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Yi Lai
- Chengdu University, China
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Minghui Li
- Chengdu University, China
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Haoxi Zhang
- Chengdu University, China
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2022.2162743
- License
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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CYBERNETICS AND SYSTEMS
no. 55,
pages 730 - 737,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Wang M., Lai Y., Li M., Zhang H., Szczerbicki E.: Toward Human Chromosome Knowledge Engine// CYBERNETICS AND SYSTEMS -Vol. 55,iss. 3 (2024), s.730-737
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2022.2162743
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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