Toward Human Chromosome Knowledge Engine

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.

Keywords:

• Causal reasoning
• chromosome part description
• human chromosomes
• knowledge engine
• probability tree model

Acknowledgement

The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions on this paper. This work was supported by the Kemoshen Science Research Program.