Knowledge Commons of Institute of Automation，CAS
| Machine Learning for Brain Imaging Genomics Methods: A Review | |
| Mei-Ling Wang1,2 | |
| Source Publication | Machine Intelligence Research
 |
| ISSN | 2731-538X |
| 2023 | |
| Volume | 20Issue: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. |
| Keyword | Brain imaging genomics machine learning multivariate analysis association analysis outcome prediction |
| DOI | 10.1007/s11633-022-1361-0 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50900 |
| Collection | 学术期刊_Machine Intelligence Research |
| Affiliation | 1.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. Machine Learning for Brain Imaging Genomics Methods: A Review[J]. Machine Intelligence Research,2023,20(1):57-78. |
| APA | Mei-Ling Wang.(2023).Machine Learning for Brain Imaging Genomics Methods: A Review.Machine Intelligence Research,20(1),57-78. |
| MLA | Mei-Ling Wang."Machine Learning for Brain Imaging Genomics Methods: A Review".Machine Intelligence Research 20.1(2023):57-78. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| MIR-2022-04-119.pdf（2285KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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