本实验室致力于整合计算与实验基因组学，旨在揭示生理状态和疾病状态下RNA合成与翻译的调控机制。课题组通过“干湿结合”的研究体系，系统性推进技术创新、基础探索与临床转化。在技术创新方面，我们开发基于人工智能（包括机器学习与深度学习）的算法，实现对功能基因组学数据的整合分析，并建立基因表达流程的新型测序技术。在基础研究方面，我们聚焦于RNA与蛋白质代谢的多层级耦合机制，解析其在发育与疾病中的分子调控网络。在临床转化方面，我们致力于发展靶向RNA翻译过程及生成蛋白质的治疗方法。目前主要研究方向包括：

1.开发深度学习模型，解析RNA生成与翻译的动态过程，探究其在生理调控与疾病发生中的功能；

2.发展核糖体图谱分析技术，揭示RNA翻译调控在癌症与神经退行性疾病中的作用；

3.探索非经典翻译区及微蛋白的进化、功能及其在疾病中的调控机制；

4.结合AI与测序技术，推动临床个体化检测与精准治疗策略的发展。

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