An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

ABSTRACT:

Introduction

Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging.

Objectives

This study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies.

Methods

We propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL).

SUBMITTER: Wang J 

PROVIDER: S-EPMC11519055 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature