Multilevel Survival Modeling With Structured Penalties for Disease Prediction From Imaging Genetics Data.
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| Title: | Multilevel Survival Modeling With Structured Penalties for Disease Prediction From Imaging Genetics Data. |
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| Authors: | Lu, Pascal1 (AUTHOR) pascal.lu@outlook.com, Colliot, Olivier1 (AUTHOR) olivier.colliot@upmc.fr |
| Source: | IEEE Journal of Biomedical & Health Informatics. Feb2022, Vol. 26 Issue 2, p798-808. 11p. |
| Subjects: | Multilevel models, Survival analysis (Biometry), Single nucleotide polymorphisms, Genetics, Mild cognitive impairment, Forecasting |
| Abstract: | This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a $\ell _2$ -penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer’s disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Journal of Biomedical & Health Informatics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 155108882 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multilevel Survival Modeling With Structured Penalties for Disease Prediction From Imaging Genetics Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Pascal%22">Lu, Pascal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.lu@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Colliot%2C+Olivier%22">Colliot, Olivier</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> olivier.colliot@upmc.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Journal+of+Biomedical+%26+Health+Informatics%22">IEEE Journal of Biomedical & Health Informatics</searchLink>. Feb2022, Vol. 26 Issue 2, p798-808. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multilevel+models%22">Multilevel models</searchLink><br /><searchLink fieldCode="DE" term="%22Survival+analysis+%28Biometry%29%22">Survival analysis (Biometry)</searchLink><br /><searchLink fieldCode="DE" term="%22Single+nucleotide+polymorphisms%22">Single nucleotide polymorphisms</searchLink><br /><searchLink fieldCode="DE" term="%22Genetics%22">Genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Mild+cognitive+impairment%22">Mild cognitive impairment</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a $\ell _2$ -penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer’s disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Journal of Biomedical & Health Informatics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/JBHI.2021.3100918 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 798 Subjects: – SubjectFull: Multilevel models Type: general – SubjectFull: Survival analysis (Biometry) Type: general – SubjectFull: Single nucleotide polymorphisms Type: general – SubjectFull: Genetics Type: general – SubjectFull: Mild cognitive impairment Type: general – SubjectFull: Forecasting Type: general Titles: – TitleFull: Multilevel Survival Modeling With Structured Penalties for Disease Prediction From Imaging Genetics Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Pascal – PersonEntity: Name: NameFull: Colliot, Olivier IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 21682194 Numbering: – Type: volume Value: 26 – Type: issue Value: 2 Titles: – TitleFull: IEEE Journal of Biomedical & Health Informatics Type: main |
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