Biomedical Data Science Graduate Training at Stanford

斯坦福大学生物医学数据科学研究生培训

• 批准号: 9901621
• 负责人: SYLVIA KATINA PLEVRITIS
• 金额: $ 30.84万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901621
• 关键词: Training; biomedical data science

 DESCRIPTION (provided by applicant): The expanded ability to collect data at all scales-molecular, cellular, tissue, organism and population-has created unparalleled opportunities for biomedical discovery. These opportunities cross all areas of research from basic science to clinical care. In response to these tremendously exciting emerging challenges in data science, Stanford University announced the creation of a new Department of Biomedical Data Science (DBDS) to begin in fall of 2015. Fundamental to the DBDS is bringing together faculty in (1) informatics and computer science, and (2) biostatistics and mathematical modeling, who work closely with a broad range of (3) biomedical science collaborators to advance knowledge. The Stanford Biomedical Informatics (BMI) training program is focused on the creation of new methods for the organization, analysis and modeling of biomedical data and knowledge. The BMI program has been a small interdisciplinary program at Stanford for more than 33 years; nonetheless, it has produced many leaders in biomedical informatics and data science. The BMI program will now have its administrative home in the DBDS, and will become the epicenter for biomedical data science training at Stanford. We are able quickly to respond to the shortage of trained scientists in biomedical data science because of a flexible curriculum, an unusually fertile set of course offerings, and a plethora of research opportunities. In this proposal, we outline a plan to engage faculty broadly across the University to create scalable mechanisms for training the next generation of biomedical data scientists, and creating a pathway for "data science" within the BMI program that stresses statistical reasoning, machine learning and data mining of biomedical data.

 描述（由申请人提供）：在所有尺度（分子、细胞、组织、生物体和人群）收集数据的能力得到了扩展，为生物医学发现创造了无与伦比的机会。这些机会跨越了从基础科学到临床护理的所有研究领域。为了应对数据科学领域这些令人兴奋的新兴挑战，斯坦福大学宣布将于2015年秋季开始建立一个新的生物医学数据科学系（DBDS）。DBDS的基础是汇集教师（1）信息学和计算机科学，（2）生物统计学和数学建模，谁与广泛的（3）生物医学科学合作者密切合作，以推进知识。斯坦福大学生物医学信息学（BMI）培训计划的重点是创造新的方法来组织，分析和建模生物医学数据和知识。BMI项目作为斯坦福大学的一个小型跨学科项目已有33年多的历史;尽管如此，它还是培养了许多生物医学信息学和数据科学领域的领导者。BMI计划现在将在DBDS中拥有其行政总部，并将成为斯坦福大学生物医学数据科学培训的中心。我们能够迅速应对生物医学数据科学训练有素的科学家短缺的问题，因为我们有灵活的课程设置，异常丰富的课程设置，以及大量的研究机会。在这份提案中，我们概述了一项计划，让整个大学的教师广泛参与，以创建可扩展的机制来培训下一代生物医学数据科学家，并在BMI计划中创建一条“数据科学”的途径，该计划强调统计推理，机器学习和生物医学数据的数据挖掘。

项目成果

The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data

• DOI: 10.1142/9789813279827_0003
• 发表时间: 2018-08
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: D. Ding;C. Simpson;S. Pfohl;Dave C. Kale;Kenneth Jung;N. Shah

Ontology-driven weak supervision for clinical entity classification in electronic health records.

• DOI: 10.1038/s41467-021-22328-4
• 发表时间: 2021-04-01
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Fries JA;Steinberg E;Khattar S;Fleming SL;Posada J;Callahan A;Shah NH
• 通讯作者: Shah NH

Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder.

外群机器学习方法识别与自闭症谱系障碍相关的非编码 DNA 中的单核苷酸变异。

• 发表时间: 2019
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Varma,Maya;Paskov,KelleyMarie;Jung,Jae-Yoon;SierraChrisman,Brianna;Stockham,NateTyler;Washington,PeterYigitcan;Wall,DennisPaul
• 通讯作者: Wall,DennisPaul

Semantic workflows for benchmark challenges: Enhancing comparability, reusability and reproducibility.

应对基准挑战的语义工作流程：增强可比性、可重用性和可重复性。

• 发表时间: 2019
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Srivastava,Arunima;Adusumilli,Ravali;Boyce,Hunter;Garijo,Daniel;Ratnakar,Varun;Mayani,Rajiv;Yu,Thomas;Machiraju,Raghu;Gil,Yolanda;Mallick,Parag
• 通讯作者: Mallick,Parag

Developing machine learning models to personalize care levels among emergency room patients for hospital admission.

• DOI: 10.1093/jamia/ocab118
• 发表时间: 2021-10-12
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Nguyen M;Corbin CK;Eulalio T;Ostberg NP;Machiraju G;Marafino BJ;Baiocchi M;Rose C;Chen JH
• 通讯作者: Chen JH