人工智能+单细胞多组学分析

代表研究工作：

Predicting transcription factor binding in single cells through deep learning

Integrated Analysis of Multimodal Single-Cell Data with Structural Similarity

SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration

Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration

S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning

Molo Pipeline：a pipeline for multi-omics analysis and machine learning

人工智能+智慧医疗

代表研究工作：

UFold: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning

LncDLSM: identification of long non-coding RNAs with deep learning-based sequence model

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

EyeDoctor: 眼科医生问诊垂直大模型

基于双分支UMamba的眼底OCTA血管提取框架

ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA–disease associations in heterogeneous graphs

人工智能+智慧农业

代表性成果：

findGSEP 

多倍体染色体长度估计方法和在线平台

平行育种系统ParaSeeds：利用大模型驱动的数字虚拟空间解码作物基因型与表型复杂关系

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育种大模型XiXiChat上线内测

多模态农业育种大模型
   ParaSeeds-V & ParaSeeds-MoE
   （内测中）

平行育种系统-数字孪生3D在线交互反馈平台

平行育种系统-多智能体 ParaSeeds-Agents
   （内测中）

人工智能+智慧工业

代表性成果：

工地安全生产监测系统

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Go Somewhere

壁画修复框架和平台

Progressive enhancement and restoration for mural images under low-light and defected conditions based on multi-receptive field strategy

Go Somewhere

产学研转化+结合

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Go Somewhere