Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification
The unknown pathogenic mechanisms of Alzheimer’s disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditio...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Artificial Cells, Nanomedicine, and Biotechnology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21691401.2025.2506591 |
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| author | Shan Huang Yixin Liu Jingyu Zhang Yiming Wang |
| author_facet | Shan Huang Yixin Liu Jingyu Zhang Yiming Wang |
| author_sort | Shan Huang |
| collection | DOAJ |
| description | The unknown pathogenic mechanisms of Alzheimer’s disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditional association analysis methods face challenges in handling nonlinear, multimodal and multi-expression data. Therefore, a multimodal attention fusion deep self-restructuring presentation (MAFDSRP) model is proposed to solve the above problem. First, multimodal brain imaging data are processed through a novel histogram-matching multiple attention mechanisms to dynamically adjust the weight of each input brain image data. Simultaneous, the genetic data are preprocessed to remove low-quality samples. Subsequently, the genetic data and fused neuroimaging data are separately input into the self-reconstruction network to learn the nonlinear relationships and perform subspace clustering at the top layer of the network. Finally, the learned genetic data and fused neuroimaging data are analysed through expression association analysis to identify AD-related biomarkers. The identified biomarkers underwent systematic multi-level analysis, revealing biomarker roles at molecular, tissue and functional levels, highlighting processes like inflammation, lipid metabolism, memory and emotional processing linked to AD. The experimental results show that MAFDSRP achieved 0.58 in association analysis, demonstrating its great potential in accurately identifying AD-related biomarkers. |
| format | Article |
| id | doaj-art-b76dc85131264ea399a2294dcbae8d52 |
| institution | Kabale University |
| issn | 2169-1401 2169-141X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Artificial Cells, Nanomedicine, and Biotechnology |
| spelling | doaj-art-b76dc85131264ea399a2294dcbae8d522025-08-20T03:53:56ZengTaylor & Francis GroupArtificial Cells, Nanomedicine, and Biotechnology2169-14012169-141X2025-12-0153123124310.1080/21691401.2025.2506591Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identificationShan Huang0Yixin Liu1Jingyu Zhang2Yiming Wang3The Second Affiliated Hospital, Harbin Medical University, Harbin, ChinaModern Education Technology Center, Harbin Medical University, Harbin, ChinaThe Fourth Affiliated Hospital of Harbin Medical University, Harbin, ChinaSchool of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaThe unknown pathogenic mechanisms of Alzheimer’s disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditional association analysis methods face challenges in handling nonlinear, multimodal and multi-expression data. Therefore, a multimodal attention fusion deep self-restructuring presentation (MAFDSRP) model is proposed to solve the above problem. First, multimodal brain imaging data are processed through a novel histogram-matching multiple attention mechanisms to dynamically adjust the weight of each input brain image data. Simultaneous, the genetic data are preprocessed to remove low-quality samples. Subsequently, the genetic data and fused neuroimaging data are separately input into the self-reconstruction network to learn the nonlinear relationships and perform subspace clustering at the top layer of the network. Finally, the learned genetic data and fused neuroimaging data are analysed through expression association analysis to identify AD-related biomarkers. The identified biomarkers underwent systematic multi-level analysis, revealing biomarker roles at molecular, tissue and functional levels, highlighting processes like inflammation, lipid metabolism, memory and emotional processing linked to AD. The experimental results show that MAFDSRP achieved 0.58 in association analysis, demonstrating its great potential in accurately identifying AD-related biomarkers.https://www.tandfonline.com/doi/10.1080/21691401.2025.2506591Alzheimer’s diseasedeep learningmultimodal fusionneuroimaging genetics |
| spellingShingle | Shan Huang Yixin Liu Jingyu Zhang Yiming Wang Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification Artificial Cells, Nanomedicine, and Biotechnology Alzheimer’s disease deep learning multimodal fusion neuroimaging genetics |
| title | Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification |
| title_full | Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification |
| title_fullStr | Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification |
| title_full_unstemmed | Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification |
| title_short | Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification |
| title_sort | multimodal attention fusion deep self reconstruction presentation model for alzheimer s disease diagnosis and biomarker identification |
| topic | Alzheimer’s disease deep learning multimodal fusion neuroimaging genetics |
| url | https://www.tandfonline.com/doi/10.1080/21691401.2025.2506591 |
| work_keys_str_mv | AT shanhuang multimodalattentionfusiondeepselfreconstructionpresentationmodelforalzheimersdiseasediagnosisandbiomarkeridentification AT yixinliu multimodalattentionfusiondeepselfreconstructionpresentationmodelforalzheimersdiseasediagnosisandbiomarkeridentification AT jingyuzhang multimodalattentionfusiondeepselfreconstructionpresentationmodelforalzheimersdiseasediagnosisandbiomarkeridentification AT yimingwang multimodalattentionfusiondeepselfreconstructionpresentationmodelforalzheimersdiseasediagnosisandbiomarkeridentification |