基于稀疏表示的精神分裂症生物标记物筛选方法

doi:10.3969/j.issn.0258-8021.2017.06.008

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Abstract Schizophrenia is a complex mental disease characterized by the division of thinking, emotion and behavior. A large number of studies have found out that genetic factors are the important causes of the disease all over the world. In order to identify the biomarkers of schizophrenia from a large number of imaging genetic data, we proposed an imaging genetic data integration analysis method based on sparse representation inspired by the sparse representation, and applied it for biomarkers selection for schizophrenia. From 208 samples, 41236 groups fMRI and 722177 groups SNP data were extracted. The generalized penalty restriction was applied to the traditional sparse representation model, and the weight factor α₁,α₂ were applied to fMRI and SNP data. With the primary aim to find out the pattern of significant association of two kinds of data under different conditions, various L_p(p=0, 0.5, 1)norm was used to solve our model. It turned out that gene DAOA and HTR2A were selected under different situations: 1. Weight factor of fMRI α₁changed from 0.35 to 0.8, 2. SNP weight α₂ was only 0.2, 3. All three norms of L_p(p=0, 0.5, 1). In addition, the results of imaging data suggested that Parietal_Inf_L and Parietal_Inf_R were associated with schizophrenia, which was same with previous studies of schizophrenia. The results show that the sparse representation of biomarkers selection method for schizophrenia is a feasible method; it may provide a new approach for the study of schizophrenia in the field of imaging genetics.

Key words： schizophrenia sparse representation single nucleotide polymorphism biomarkers

Received: 12 April 2017

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R318

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	Wu Jie
	Wei Fengxian
	Fu Ling

Cite this article:

Wu Jie,Wei Fengxian,Fu Ling. A Selection Method of Biomarkers for Schizophrenia Based on Sparse Representation[J]. Chinese Journal of Biomedical Engineering, 2017, 36(6): 692-696.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.06.008 OR http://cjbme.csbme.org/EN/Y2017/V36/I6/692

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