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Deep Learning-Guided crRNA Design Enables an AND Logic-Gated Autocatalytic CRISPR Network for Dual-miRNA Detection

delete2026-08-10
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PRE
AI
J
Junyuan Yang
Y
Ying Liu
H
Hao Jiang
A
Anyi Li
吕
吕雪飞 (Xuefei Lv) *
DOI：10.1016/j.snb.2026.140668delete
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Abstract

Abstract

En 中文
• DiagCRISPR, a deep learning platform featuring locally connected neural networks was developed as a pre-screening tool to achieve rational crRNA design for short, PAM-free ssDNA substrates. • A programmable upstream circuit driven by PER-based AND logic gate enables precise compression of dual-miRNA (miR-21 and miR-155) inputs into a single optimized molecular trigger. • The computation-guided crRNA optimization is successfully coupled with a split-crRNA-mediated autocatalytic CRISPR network, allowing exponential signal amplification and sub-picomolar dual-miRNA diagnostics.

Journal

Sensors and Actuators B-Chemical cover
Sensors and Actuators B-Chemical
IF:
7.7
Papers:
3.3W
Citations:
12.6W

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