Abstract
“In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive datasets to train ML mo dels and advocate for the standardization of data to support robust model develo pment.
NETL
NSTL