Graph machine learning for integrated multi-omics analysis

Abstract

Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches—including graph neural networks—potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.

Optimizing Model Performance and Interpretability: an application to biological data classification

“…Such techniques have been widely used in data analysis problems across virtually all data-rich domains. A large suite of machinelearning algorithms has been developed, such as neural network-based methods [1], Support Vector Machine (SVM) [2], regression models [3], decision tree-based methods [4,5,6], for various data-analysis problems. It is well understood that different problems may require different machine-learning techniques.…”