Universal Cell Embeddings: A Foundation Model for Cell Biology
Rosen Y, Roohani Y, Agrawal A, Samotorcan L, Tabula Sapiens Consortium, Quake SR, Quake SR, Leskovec J. · bioRxiv · 2023-11-29
UCE：跨物种零样本细胞嵌入模型（Leskovec/Quake）。
年份：2023
共 8 条，按时间倒序。
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
Wen H, Tang W, Dai X, Ding J, Jin W, Xie Y, Tang J. · bioRxiv · 2023-10-05
CellPLM：引入空间关系的细胞语言模型。
Learning single-cell perturbation responses using neural optimal transport.
Bunne C, Stark SG, Gut G, Del Castillo JS, Levesque M, Lehmann KV, Pelkmans L, Krause A, Rätsch G. · Nature methods · 2023-09-28
CellOT：神经最优传输预测扰动反应。
Cell2Sentence: Teaching Large Language Models the Language of Biology
Levine D et al. · bioRxiv · 2023-09-14
Cell2Sentence：把细胞转为“句子”供大语言模型学习。
Predicting transcriptional outcomes of novel multigene perturbations with GEARS.
Roohani Y, Huang K, Leskovec J. · Nature biotechnology · 2023-08-17
GEARS：知识图谱图神经网络预测多基因扰动。
Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma.
Wang Z, Wang Y, Chang M, Wang Y, Liu P, Wu J, Wang G, Tang X, Hui X, Liu P, Guo X, Xing B, Wang Y, Han Z, Ma W. · Neuro-oncology · 2023-07-01
脑转移细胞起源与驱动。
Transfer learning enables predictions in network biology.
Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT. · Nature · 2023-05-31
Geneformer：支持“计算机模拟敲除”的单细胞基础模型。
Predicting cellular responses to complex perturbations in high-throughput screens.
Lotfollahi M et al. · Molecular systems biology · 2023-05-08
CPA：组合扰动自编码器，预测药物/剂量/组合反应。