CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Machine Learning for Brain Imaging Genomics Methods: A Review
Mei-Ling Wang1,2; Wei Shao1,2; Xiao-Ke Hao3; Dao-Qiang Zhang1,2
Source PublicationMachine Intelligence Research
ISSN2731-538X
2023
Volume20Issue:1Pages:57-78
Abstract

In the past decade, multimodal neuroimaging and genomic techniques have been increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotypic measures derived from structural and functional brain imaging. This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans. It is well known that machine learning is a powerful tool in the data-driven association studies, which can fully utilize priori knowledge (intercorrelated structure information among imaging and genetic data) for association modelling. In addition, the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is explored. In this paper, the related background and fundamental work in imaging genomics are first reviewed. Then, we show the univariate learning approaches for association analysis, summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning, and present methods for joint association analysis and outcome prediction. Finally, this paper discusses some prospects for future work.

KeywordBrain imaging genomics machine learning multivariate analysis association analysis outcome prediction
DOI10.1007/s11633-022-1361-0
Sub direction classification其他
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Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55966
Collection学术期刊_Machine Intelligence Research
Affiliation1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.Key Laboratory of Pattern Analysis and Machine Intelligence, Ministry of Industry and Information Technology, Nanjing 211106, China
3.School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Recommended Citation
GB/T 7714
Mei-Ling Wang,Wei Shao,Xiao-Ke Hao,et al. Machine Learning for Brain Imaging Genomics Methods: A Review[J]. Machine Intelligence Research,2023,20(1):57-78.
APA Mei-Ling Wang,Wei Shao,Xiao-Ke Hao,&Dao-Qiang Zhang.(2023).Machine Learning for Brain Imaging Genomics Methods: A Review.Machine Intelligence Research,20(1),57-78.
MLA Mei-Ling Wang,et al."Machine Learning for Brain Imaging Genomics Methods: A Review".Machine Intelligence Research 20.1(2023):57-78.
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