Prioritization of MultiLevel Risk Factors for Obesity Obesity has become a significant threat to health. Identifying and understanding the underlying obesity risk factors (ORFs) are crucial for optimizing prevention, intervention and treatment for obesity. Most existing methodological approaches to risk factor analysis are employed within the single task learning (STL) framework to learn a ranked list of ORFs for a whole population. However, obesity is a multifaced health outcome. Some ORFs are highly specific to a certain subpopulation and others are universal to the entire population. Multitask learning (MTL) framework offers a solution to connect multiple related tasks. Within the MTL framework, we implement two tailormade models, i.e., multitask feature learning (MTFL) and clustered multitask learning (CMTL), to conduct ORFs analysis. The former is capable of finding the universal ORFs for all subpopulations without sacrificing the uniqueness of each subpopulation. The latter uncovers the grouping structure and conducts multilevel ORFs analysis simultaneously. Experiments on a public behavioral dataset demonstrate a superior performance of our methods in prioritizing multilevel ORFs.

Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s 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 Alzheimeru0027s disease. Multitask 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 multitask learning approach via a joint sparsityinducing 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.