Structured sparse low-rank regression model for brain-wide and genome-wide associations

Xiaofeng Zhu, Heung-Il Suk, Heng Huang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

With the advances of neuroimaging techniques and genome sequences understanding,the phenotype and genotype data have been utilized to study the brain diseases (known as imaging genetics). One of the most important topics in image genetics is to discover the genetic basis of phenotypic markers and their associations. In such studies,the linear regression models have been playing an important role by providing interpretable results. However,due to their modeling characteristics,it is limited to effectively utilize inherent information among the phenotypes and genotypes,which are helpful for better understanding their associations. In this work,we propose a structured sparse lowrank regression method to explicitly consider the correlations within the imaging phenotypes and the genotypes simultaneously for Brain- Wide and Genome-Wide Association (BW-GWA) study. Specifically,we impose the low-rank constraint as well as the structured sparse constraint on both phenotypes and phenotypes. By using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset,we conducted experiments of predicting the phenotype data from genotype data and achieved performance improvement by 12.75% on average in terms of the rootmean- square error over the state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages344-352
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
Publication statusPublished - 2016
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

Fingerprint

Rank Regression
Neuroimaging
Phenotype
Brain
Regression Model
Genome
Genes
Genotype
Imaging techniques
Linear regression
Imaging
Alzheimer's Disease
Experiments
Linear Regression Model
Genetics
Regression
Modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhu, X., Suk, H-I., Huang, H., & Shen, D. (2016). Structured sparse low-rank regression model for brain-wide and genome-wide associations. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 344-352). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_40

Structured sparse low-rank regression model for brain-wide and genome-wide associations. / Zhu, Xiaofeng; Suk, Heung-Il; Huang, Heng; Shen, Dinggang.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 344-352 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, X, Suk, H-I, Huang, H & Shen, D 2016, Structured sparse low-rank regression model for brain-wide and genome-wide associations. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9900 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 344-352, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-46720-7_40
Zhu X, Suk H-I, Huang H, Shen D. Structured sparse low-rank regression model for brain-wide and genome-wide associations. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS. Springer Verlag. 2016. p. 344-352. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46720-7_40
Zhu, Xiaofeng ; Suk, Heung-Il ; Huang, Heng ; Shen, Dinggang. / Structured sparse low-rank regression model for brain-wide and genome-wide associations. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. pp. 344-352 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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