A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.

ABSTRACT:

Background

Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts.

Results

We develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in can

SUBMITTER: Kennedy L 

PROVIDER: S-EPMC11817629 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature