Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model

The ADNI

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

4 Citations (Scopus)

Abstract

With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer’s Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings
PublisherSpringer Verlag
Pages287-302
Number of pages16
Volume10229 LNCS
ISBN (Print)9783319569697
DOIs
StatePublished - 2017
Event21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017 - Hong Kong, China
Duration: May 3 2017May 7 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10229 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017
CountryChina
CityHong Kong
Period5/3/175/7/17

Fingerprint

Neuroimaging
Predictive Model
Genotype
Phenotype
Brain
Neurodegenerative diseases
Imaging techniques
Alzheimer's Disease
Learning systems
Throughput
Imaging
Prediction
High Throughput
Disorder
Machine Learning
Learning
Model
Genetics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

The ADNI (2017). Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. In Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings (Vol. 10229 LNCS, pp. 287-302). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10229 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-56970-3_18

Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. / The ADNI.

Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings. Vol. 10229 LNCS Springer Verlag, 2017. p. 287-302 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10229 LNCS).

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

The ADNI 2017, Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. in Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings. vol. 10229 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10229 LNCS, Springer Verlag, pp. 287-302, 21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017, Hong Kong, China, 5/3/17. https://doi.org/10.1007/978-3-319-56970-3_18
The ADNI. Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. In Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings. Vol. 10229 LNCS. Springer Verlag. 2017. p. 287-302. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-56970-3_18
The ADNI. / Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings. Vol. 10229 LNCS Springer Verlag, 2017. pp. 287-302 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer’s Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to.",
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