A new sparse simplex model for brain anatomical and genetic network analysis

Heng Huang, Jingwen Yan, Feiping Nie, Jin Huang, Weidong Cai, Andrew J. Saykin, Li Shen

科研成果: 书/报告/会议事项章节会议稿件同行评审

13 引用 (Scopus)

摘要

The Allen Brain Atlas (ABA) database provides comprehensive 3D atlas of gene expression in the adult mouse brain for studying the spatial expression patterns in the mammalian central nervous system. It is computationally challenging to construct the accurate anatomical and genetic networks using the ABA 4D data. In this paper, we propose a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns. Our new approach addresses the shift-invariant and parameter tuning problems, which are notorious in the existing network analysis methods, such that the proposed model is more suitable for solving practical biomedical problems. We validate our new model using the 4D ABA data, and the network construction results show the superior performance of the proposed sparse simplex model.

源语言英语
主期刊名Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
625-632
页数8
版本PART 2
DOI
出版状态已出版 - 2013
已对外发布
活动16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, 日本
期限: 22 9月 201326 9月 2013

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
编号PART 2
8150 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
国家/地区日本
Nagoya
时期22/09/1326/09/13

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