ArticleScientific reports2022
Predictive classification of Alzheimer's disease using brain imaging and genetic data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Imaging Genetics in Alzheimer's Disease: A Systematic Review and Current Update.CNS & neurological disorders drug targets · 2024Pooled it
- PCA-Enhanced Deep Features for Alzheimer's Disease Stage Classification with EFMM.Diagnostics (Basel, Switzerland) · 2026Article
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment.Medical & biological engineering & computing · 2025Article
- Prediction of mild cognitive impairment using blood multi-omics data.Frontiers in genetics · 2025Article
- Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion.BioData mining · 2024Article
- Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging.NPJ digital medicine · 2024Review
- Article
- Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review.Health information science and systems · 2023Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis.Computer science & information technology · 2022Article
- Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For now, Alzheimer's disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few studies combine brain imaging with genetic features for disease diagnosis. In order to accurately identify AD, healthy control (HC) and the two stages of mild cognitive impairment (MCI: early MCI, late MCI) combined with brain imaging and genetic characteristics, we proposed an integrated Fisher score and multi-modal multi-task feature selection research method. We learned first genetic features with Fisher score to perform dimensionality reduction in order to solve the problem of the large difference between the feature scales of genetic and brain imaging. Then we learned the potential related features of brain imaging and genetic data, and multiplied the selected features with the learned weight coefficients. Through the feature selection program, five imaging and five genetic features were selected to achieve an average classification accuracy of 98% for HC and AD, 82% for HC and EMCI, 86% for HC and LMCI, 80% for EMCI and LMCI, 88% for EMCI and AD, and 72% for LMCI and AD. Compared with only using imaging features, the classification accuracy has been improved to a certain extent, and a set of interrelated features of brain imaging phenotypes and genetic factors were selected.
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