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Feature-aware Multi-task feature learning for Predicting Cognitive Outcomes in Alzheimer’s disease

Full Text: BIBM2019.pdf PDF

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Multi-task based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multi-task learning approach via a joint sparsity-inducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.

Citation

P. Cao, S. Tang, M. Huang, J. Yang, D. Zhao, A. Trabelsi, O. Zaiane. "Feature-aware Multi-task feature learning for Predicting Cognitive Outcomes in Alzheimer’s disease". IEEE International Conference on Bioinformatics and Biomedicine , (ed: Illhoi Yoo, Jinbo Bi, Xiaohua Hu), pp n/a, November 2019.

Keywords: Alzheimer’s disease, Regression model, Multi-task learning, Magnetic resonance imaging, Biomarkers discovery
Category: In Conference
Web Links: doi
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BibTeX

@incollection{Cao+al:BIBM19,
  author = {Peng Cao and Shanshan Tang and Min Huang and Jinzhu Yang and Danzhe
    Zhao and Amine Trabelsi and Osmar R. Zaiane},
  title = {Feature-aware Multi-task feature learning for Predicting Cognitive
    Outcomes in Alzheimer’s disease},
  Editor = {Illhoi Yoo, Jinbo Bi, Xiaohua Hu},
  Pages = {n/a},
  booktitle = {IEEE International Conference on Bioinformatics and Biomedicine
    },
  year = 2019,
}

Last Updated: September 15, 2020
Submitted by Sabina P

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