CRISPR-FMC: a dual-branch hybrid network for predicting CRISPR-Cas9 on-target activity.

ABSTRACT:

Introduction

Accurately predicting the on-target activity of sgRNAs remains a challenge in CRISPR-Cas9 applications, due to the limited generalization of existing models across datasets, small-sample settings, and complex sequence contexts. Current methods often rely on shallow architectures or unimodal encodings, limiting their ability to capture the intricate dependencies underlying Cas9-mediated cleavage.

Methods

We present CRISPR-FMC, a dual-branch hybrid neural network that integrates One-hot encoding with contextual embeddings from a pre-trained RNA-FM model. Multi-scale convolution (MSC), BiGRU, and Transformer blocks are employed to extract hierarchical sequence features, while a bidirectional cross-attention mechanism with a residual feedforward network enhances mul

SUBMITTER: Li C 

PROVIDER: S-EPMC12426269 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature