Prior knowledge informs graph neural networks to improve phenotype prediction from proteomics.

ABSTRACT: High-throughput proteomics data provides dense individual-level molecular readouts, enabling the development of machine learning models for predicting diverse phenotypes relevant to patient health. Proteins interact in the cell in complex, nonlinear relationships that may not be reflected by linear models or simple machine learning approaches, highlighting the potential for more expressive deep neural networks to improve performance. Despite this possibility, in practice, developing neural network approaches in biological domains has been a significant challenge. We developed a deep learning framework for predicting disease-related traits from protein expression data using an innovative model architecture designed to exploit structured biological knowledge. The core of the model is a graph

SUBMITTER: Dastidar PG 

PROVIDER: S-EPMC12676398 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature