Collecting comprehensive data sets of the same subject has become a standard in
neuroscience research and uncovering multivariate relationships among collected data sets …

Deep learning has recently been used for the analysis of neuroimages, such as structural
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …

In recent years, the advances in data collection and statistical analysis promotes canonical
correlation analysis (CCA) available for more advanced research. CCA is the main …

Brain imaging genomics is an emerging data science field, where integrated analysis of
brain imaging and genomics data, often combined with other biomarker, clinical, and …

Recent studies show that multi-modal data fusion techniques combining information from
diverse sources are helpful to diagnose and predict complex brain disorders. However, most …

Either traditional learning methods or deep learning methods have been widely applied for
the early Alzheimer's disease (AD) diagnosis, but these methods often suffer from the issue …

As a complex neural network system, the brain regions and genes collaborate to effectively
store and transmit information. We abstract the collaboration correlations as the brain region …

The inherent heterogeneity of cancer contributes to highly variable responses to any
anticancer treatments. This underscores the need to first identify precise biomarkers through …

Alzheimer's disease (AD) is an incurable neurodegenerative disease, so it is important to
intervene in the early stage of the disease. Brain imaging genetics is an effective technique …

In the past decade, multimodal neuroimaging and genomic techniques have been
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …