Stanford Tissue Mapping Center

斯坦福大学组织绘图中心

• 批准号: 10414673
• 负责人: GARRY P NOLAN
• 金额: $ 10万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10414673
• 关键词: Cells; Colon; Data; Data Set; Development; Funding; Future; Hour; Human BioMolecular Atlas Program; Label; Large Intestine; Liquid Chromatography; Manuals; Mass Spectrum Analysis; Research Proposals; Running; Sampling; Savings; Small Intestines; System; Tissues; Universities; base; cell type; computer science; computerized tools; deep learning; improved; learning network; member; nano; success; tool

HuBMAP Supplemental Research Proposal Overview We are requesting supplemental funds for two items: 1. Development of a new computational tool based on a recently developed deep-learning network called ORCA to use spatial features with single-cell expression data from CODEX to more accurately transfer cell type annotations to unlabeled CODEX datasets (50% funds are requested to fund a member from Prof. Jure Leskovec from Computer Science at Stanford University to join our efforts at the Stanford TMC). This tool has already been used to transfer cell type annotations to unlabeled HuBMAP donor small intestine and large intestine single cell CODEX data, already saving nearly 100 hours of annotations required for annotating 2 donors datasets, yet does not incorporate spatial annotations to help with cell type annotations. 2. An EvoSep Liquid Chromatography system (50% funds are requested) for scProteomics. It is one of the few systems which can run a true low nano flow rate gradient ( <100 nl/min). Low nano flow can dramatically improve sensitivity which is the key for the success of scProteomics using mass spectrometry.

HuBMAP补充研究计划概述

项目成果

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular switches.

• DOI: 10.1073/pnas.2112979119
• 发表时间: 2022-05-03
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Potential and risks of artificial intelligence models: Common in medicine practice and special in pediatric urology.

人工智能模型的潜力和风险：在医学实践中常见，在儿科泌尿外科中特殊。

• DOI: 10.1016/j.jpurol.2023.06.005
• 发表时间: 2023
• 期刊: Journal of pediatric urology
• 影响因子: 2
• 作者: Wen,Yi;Di,Huajie
• 通讯作者: Di,Huajie

Foundation models for generalist medical artificial intelligence

• DOI: 10.1038/s41586-023-05881-4
• 发表时间: 2023-04-13
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Moor, Michael;Banerjee, Oishi;Rajpurkar, Pranav
• 通讯作者: Rajpurkar, Pranav