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Article summary:
1. This article proposes a novel feature construction method and genetic multi-kernel SVM model to identify important brain voxels that can classify Alzheimer's Disease (AD), Early Mild Cognitive Impairment (EMCI) and Healthy Control (HC).
2. The proposed model uses magnetic resonance imaging (MRI) imaging and gene information to amplify the differences among AD, EMCI and HC groups.
3. The findings of this study identified some brain regions affected by AD, as well as significant susceptibility genes associated with AD, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX.
Article analysis:
This article is a reliable source of information on the use of MRI imaging and gene data for feature fusion and detection in Alzheimer's Disease. The authors have provided a detailed description of their proposed method and its results, which are supported by evidence from experiments conducted on real datasets. Furthermore, the authors have discussed potential limitations of their approach such as the small sample size used in the experiments.
The article is also unbiased in its reporting; it presents both sides equally without any promotional content or partiality towards one side or another. Additionally, possible risks are noted throughout the article, such as the potential for false positives due to small sample sizes used in the experiments.
In conclusion, this article is a reliable source of information on feature fusion and detection in Alzheimer's Disease using MRI imaging and gene data.