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（由适用提供）：扩展的能力，可以在所有尺度分子，细胞，组织，组织和人口中收集数据，从而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。为了应对这些令人振奋的数据科学挑战，斯坦福大学宣布创建新的生物医学数据科学系（DBD），将于2015年秋季开始。DBD的基础是（1）信息和计算机科学的教职员工，以及（2）与Biotical和数学模型合作，与Bigalsifors合作，与BIG Science（3）相比（3）。斯坦福生物医学信息学（BMI）培训计划的重点是为组织，分析和建模生物医学数据和知识创建新方法。 BMI计划是斯坦福大学的小型跨学科计划，已有33多年的历史了。尽管如此，它还是培养了许多生物医学信息和数据科学领域的领导者。 BMI计划现在将在DBD中拥有其行政管理，并将成为斯坦福大学生物医学数据科学培训的中心。我们能够迅速回应生物医学数据科学中训练有素的科学家，这是因为灵活的课程，一套异常肥沃的课程以及大量的研究机会。在这项建议中，我们概述了一项计划，旨在在整个大学中广泛参与教师，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计划中为“数据科学”创建途径，以强调统计推理，机器学习和生物医学数据的数据挖掘。

项目成果

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

Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism.

联盟博弈论有助于识别与自闭症相关的非编码变异。

• DOI: 10.1177/1178222619832859
• 发表时间: 2019
• 期刊: Biomedical informatics insights
• 作者: Sun,MinWoo;Gupta,Anika;Varma,Maya;Paskov,KelleyM;Jung,Jae-Yoon;Stockham,NateT;Wall,DennisP
• 通讯作者: Wall,DennisP

An empirical characterization of fair machine learning for clinical risk prediction.

• DOI: 10.1016/j.jbi.2020.103621
• 发表时间: 2021-01
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Pfohl SR;Foryciarz 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