TITLE: A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets 

ABSTRACT: In recent years, a comprehensive study of multi-view datasets (e.g.,
multi-omics and imaging scans) has been a focus and forefront in biomedical
research. State-of-the-art biomedical technologies are enabling us to collect
multi-view biomedical datasets for the study of complex diseases. While all the
views of data tend to explore complementary information of a disease,
multi-view data analysis with complex interactions is challenging for a deeper
and holistic understanding of biological systems. In this paper, we propose a
novel generalized kernel machine approach to identify higher-order composite
effects in multi-view biomedical datasets. This generalized semi-parametric (a
mixed-effect linear model) approach includes the marginal and joint Hadamard
product of features from different views of data. The proposed kernel machine
approach considers multi-view data as predictor variables to allow more
thorough and comprehensive modeling of a complex trait. The proposed method can
be applied to the study of any disease model, where multi-view datasets are
available. We applied our approach to both synthesized datasets and real
multi-view datasets from adolescence brain development and osteoporosis study,
including an imaging scan dataset and five omics datasets. Our experiments
demonstrate that the proposed method can effectively identify higher-order
composite effects and suggest that corresponding features (genes, region of
interests, and chemical taxonomies) function in a concerted effort. We show
that the proposed method is more generalizable than existing ones.
In recent years, a comprehensive study of multi-view datasets (e.g.,
multi-omics and imaging scans) has been a focus and forefront in biomedical
research. State-of-the-art biomedical technologies are enabling us to collect
multi-view biomedical datasets for the study of complex diseases. While all the
views of data tend to explore complementary information of a disease,
multi-view data analysis with complex interactions is challenging for a deeper
and holistic understanding of biological systems. In this paper, we propose a
novel generalized kernel machine approach to identify higher-order composite
effects in multi-view biomedical datasets. This generalized semi-parametric (a
mixed-effect linear model) approach includes the marginal and joint Hadamard
product of features from different views of data. The proposed kernel machine
approach considers multi-view data as predictor variables to allow more
thorough and comprehensive modeling of a complex trait. The proposed method can
be applied to the study of any disease model, where multi-view datasets are
available. We applied our approach to both synthesized datasets and real
multi-view datasets from adolescence brain development and osteoporosis study,
including an imaging scan dataset and five omics datasets. Our experiments
demonstrate that the proposed method can effectively identify higher-order
composite effects and suggest that corresponding features (genes, region of
interests, and chemical taxonomies) function in a concerted effort. We show
that the proposed method is more generalizable than existing ones.
A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets
http://arxiv.org/abs/2004.14031v1