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

arXiv: https://arxiv.org/abs/2004.14031 · HTML: https://mlsanity.com/paper/2004.14031/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression](https://mlsanity.com/paper/2510.16093/) (2025)
- [A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery](https://mlsanity.com/paper/2201.05060/) (2022)
- [Weighted Scaling Approach for Metabolomics Data Analysis](https://mlsanity.com/paper/2208.00603/) (2022)
- [Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning](https://mlsanity.com/paper/2511.03693/) (2025)
- [Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology](https://mlsanity.com/paper/2603.14416/) (2026)

## Abstract (arXiv)

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.