ArticleScientific reports2017
A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A novel method to disentangle tightly linked risk and resilience genes for brain disorders: application to Alzheimer's disease.Molecular psychiatry · 2026Article
- Article
- Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.Neurobiology of aging · 2023Article
- A review of brain imaging biomarker genomics in Alzheimer's disease: implementation and perspectives.Translational neurodegeneration · 2022Review
- Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.Journal of healthcare engineering · 2022Article
- Data analysis with Shapley values for automatic subject selection in Alzheimer's disease data sets using interpretable machine learning.Alzheimer's research & therapy · 2021Article
- PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.Journal of Alzheimer's disease : JAD · 2021Article
- Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review.Brain communications · 2021Review
- Imaging biomarkers in neurodegeneration: current and future practices.Alzheimer's research & therapy · 2020Review
- Using a Data-Driven Approach to Estimate Second-Language Proficiency From Brain Activation: A Functional Near-Infrared Spectroscopy Study.Frontiers in neuroscience · 2020Article
- Genetic Variants Detection Based on Weighted Sparse Group Lasso.Frontiers in genetics · 2020Article
- Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data.IEEE transactions on medical imaging · 2019Article
- Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.Human brain mapping · 2019Article
- Machine learning studies on major brain diseases: 5-year trends of 2014-2018.Japanese journal of radiology · 2019Review
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
- Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.Scientific reports · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer's disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.
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