科研 - 付 来义
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
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Integrated Analysis of Multimodal Single-Cell Data with Structural Similarity
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SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration
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Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration
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S3RL: Separable Spatial Single-cell Representation Learning via Graph Transformer and Hyperspherical Prototype Clustering
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Molo Pipeline：a pipeline for multi-omics analysis and machine learning
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Go Somewhere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
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LncDLSM: identification of long non-coding RNAs with deep learning-based sequence model
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KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations
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EyeDoctor: 眼科医生问诊垂直大模型
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基于双分支UMamba的眼底OCTA血管提取框架
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ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA–disease associations in heterogeneous graphs
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Go Somewhere人工智能+智慧农业
代表性成果：
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
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多模态农业育种大模型
ParaSeeds-V & ParaSeeds-MoE
（内测中）
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平行育种系统-数字孪生3D在线交互反馈平台
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平行育种系统-多智能体 ParaSeeds-Agents
（内测中）
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Go Somewhere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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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
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产学研转化+结合
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