Bio-AI

论文阅读记录stMMR 标题：stMMR: accurate and robust saptial domain identification from spatially resolved transcriptomics with multimodal feature representation 作者：Shandong University 发表会议/期刊：GigaScience 年份：2024 链接：stMMR 主要内容简介 Motivation:融合多模态困难，多模态数据间具有显著异质性，而且在数据尺度和分辨率上存在差异 方法与创新点 总体框架：3 steps: multimodal feature embedding, feature fusion, and feature reconstruction 方法概述： Multimodal feature embedding gene expression: $ G \in \mathbb{R}^{n \times p} $ (n spots, p genes) histological...

论文阅读记录Bering 标题：Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings 作者：Harvard University 发表会议/期刊：Nature Communications 年份：2025 链接：Bering 主要内容简介 Motivation:Some tissues have densely packed cells with unclear boundaries, making it difficult to perform accurate segmentation Task:Cell segmentation and annotation for spatial transcriptomics 方法与创新点 总体框架： 方法概述： 图构建，NGC 图卷积和全连接网络 节点分类 边嵌入由三部分组成- node representation-...

论文阅读记录IE-HERCL 标题：Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning for Spatial Domain Identification in Spatial Transcriptomics 作者：Central South University 发表会议/期刊：IJCAI 年份：2025 链接：IE-HERCL 主要内容简介 Motivation: Exisisting methods fail to account for complex interdependencies between modalities. 方法与创新点 总体框架： 方法概述： Multimodal Feature Representation Learning utilize AE to extract & Loss function L_rec: $$L^g_{rec} = | X_{g,i} -...

论文阅读记录stLearn 标题：Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues 作者：The University of Queensland 发表会议/期刊：Nature Communications 年份：2023 链接：stLearn 主要内容简介 Motivation: the (re)construction of spatio-temporal trajectories the study of cell–cell interactions the improvement of spatial data quality by imputation 方法与创新点 总体框架： 方法概述： pseudo-time-space (PSTS) for spatio-temporal trajectory inference DPT 与 Space...

论文阅读记录GraphST 标题：Spatially informed clustering, integration,and deconvolution of spatial transcriptomics with GraphST 作者：National University of Singapore (NUS) 发表会议/期刊：Nature Communications 年份：2023 链接：GraphST 主要内容简介 Motivation: 反卷积未利用空间信息 多样本整合未利用空间信息 方法与创新点 总体框架： 方法概述： graph self-supervised contrastive learning framework： data augmentation GNN-based encoder for representation learning self-supervised contrastive learning for representation refinement ...

论文阅读记录DeepST 标题：DeepST: identifying spatial domains in spatial transcriptomics by deep learning 作者：Harbin Institute of Technology 发表会议/期刊：Nucleic Acids Research 年份：2022 链接：DeepST 主要内容简介 Motivation: 先前方法主要依赖线性主成分分析来提取基因表达的高变特征，因此无法建模复杂的非线性相互作用 未充分利用空间信息，且在预测组织结构方面存在局限 大多数分析大量ST数据的空间方法无法正确校正批次效应，且不能处理其他空间组学数据，使其通用性不足 方法与创新点 总体框架： 方法概述： Spatial data...

论文阅读记录STAGATE 标题：Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder 作者：University of Chinese Academy of Sciences 发表会议/期刊：Nature Communications 年份：2022 链接：STAGATE 主要内容简介 Motivation: 邻域相似性是预定义的，无法自适应学习。 方法与创新点 总体框架： 方法概述： Construction of SNN (Construction of cell type-aware SNN (optional)) Graph attention auto-encoder Encoder Decoder (迭代和encoder相似) graph attention layer -graph...

论文阅读记录Spatial-MGCN 标题：Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism 作者：Hunan University 发表会议/期刊：Briefings in Bioinformatics 年份：2023 链接：Spatial-MGCN 主要内容简介 Motivation：While increasingly computational methods have been developed for spatial domain detection, most of them cannot adaptively learn the complex relationship between gene expression and spatial information, leading to sub-optimal...

论文阅读记录STMGraph 标题：STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model 作者：Fujian Agriculture and Forestry University 发表会议/期刊：Briefings in Bioinformatics 年份：2025 链接：STMGraph 主要内容简介 Motivation：the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and...

📖 论文阅读记录1. 基本信息 论文题目：Multi-View Adaptive Fusion Network for Spatially Resolved Transcriptomics Data Clustering 作者/机构：China University of Geosciences 会议/期刊：IEEE TRANSACTIONS 年份：DECEMBER 2024 论文链接：MAFN_IEEE 2. 方法总览 总体框架： 关键技术路线： spatial graph G1(euclidean distance & r), feature graph G2(cosine similarity & kNN) Inter-View Complementary Features Learning (GCN) Intra-View Discriminative Features Learning(GCN + Lc –> (S-I)^2) Cross-View Attention Module...