# Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression

arXiv: https://arxiv.org/abs/2510.16093 · HTML: https://mlsanity.com/paper/2510.16093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 74 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis](https://mlsanity.com/paper/1609.04699/) (2016)
- [A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets](https://mlsanity.com/paper/2004.14031/) (2020)
- [Supervised Multiple Kernel Learning approaches for multi-omics data integration](https://mlsanity.com/paper/2403.18355/) (2024)
- [Influence Function and Robust Variant of Kernel Canonical Correlation Analysis](https://mlsanity.com/paper/1705.04194/) (2017)

## Built on by (most cited first, 1 of 1)

- [RKUM: An R Package for Robust Kernel Unsupervised Methods](https://mlsanity.com/paper/2511.03216/) (2025)

## Abstract (arXiv)

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. In this paper, we analyze lung cancer multi-omics datasets from The Cancer Genome Atlas (TCGA). Using four statistical methods, LIMMA, the T test, Canonical Correlation Analysis (CCA), and the Wilcoxon test, we identified differentially expressed genes across gene expression, DNA methylation, and miRNA expression data. We then integrated these multi-omics data using the Kernel Machine Regression (KMR) approach. Our findings reveal significant interactions among the three omics: gene expression, miRNA expression, and DNA methylation in lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. Among these, eight genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that could play a crucial role in the treatment of lung cancer. These proposed drugs are also supported by the findings of other independent studies, which underscore their potential efficacy in the fight against lung cancer.