Advances, obstacles, and opportunities for machine learning in proteomics

Cell Rep Phys Sci. 2022 Oct 19;3(10):101069. doi: 10.1016/j.xcrp.2022.101069. Epub 2022 Sep 22.


The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.