KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality.

ABSTRACT:

Motivation

Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification.

Results

We present a model named KR4SL to predict SL par

SUBMITTER: Zhang K 

PROVIDER: S-EPMC10311291 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature