Network Learning for Biomarker Discovery

Survey/review study Network Learning for Biomarker Discovery Yulian Ding 1, Minghan Fu 1, Ping Luo 2, and Fang-Xiang Wu 1,3,4,* 1 Division of Biomedical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 2 Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada 3 Department of Computer Sciences, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 4 Department of Mechanical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada * Correspondence: faw341@mail.usask.ca Received: 14 October 2022 Accepted: 5 December 2022 Published: Abstract: Everything is connected and thus networks are instrumental in not only modeling complex systems with many components, but also accommodating knowledge about their components. Broadly speaking, network learning is an emerging area of machine learning to discover knowledge within networks. Although networks have permeated all subjects of sciences, in this study we mainly focus on network learning for biomarker discovery. We first overview methods for traditional network learning which learn knowledge from networks with centrality analysis. Then, we summarize the network deep learning, which are powerful machine learning models that integrate networks (graphs) with deep neural networks. Biomarkers can be placed in proper biological networks as vertices or edges and network learning applications for biomarker discovery are discussed. We finally point out some promising directions for future work about network learning.