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)：扩大了在所有尺度--分子、细胞、组织、生物体和种群--收集数据的能力，为生物医学发现创造了无与伦比的机会。这些机会涉及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新挑战，斯坦福大学宣布成立一个新的生物医学数据科学系(DBDS)，将于2015年秋季开始。DBDS的基础是将(1)信息学和计算机科学以及(2)生物统计学和数学建模方面的教师聚集在一起，他们与广泛的生物医学科学合作者密切合作，以促进知识的发展。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造新的方法。33年来，BMI项目一直是斯坦福大学的一个小型跨学科项目；尽管如此，它还是培养出了许多生物医学信息学和数据科学领域的领军人物。BMI项目现在的管理总部将设在DBDS，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方面训练有素的科学家的短缺，因为我们有灵活的课程设置、异常丰富多彩的课程设置和过多的研究机会。在这份提案中，我们概述了一项计划，即在整个大学范围内动员教师，创建可扩展的机制，以培训下一代生物医学数据科学家，并在BMI项目中创建一条强调生物医学数据的统计推理、机器学习和数据挖掘的“数据科学”途径。

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

Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition.

• DOI: 10.3390/jpm10030086
• 发表时间: 2020-08-13
• 期刊: Journal of personalized medicine
• 作者: Washington P;Leblanc E;Dunlap K;Penev Y;Kline A;Paskov K;Sun MW;Chrisman B;Stockham N;Varma M;Voss C;Haber N;Wall DP
• 通讯作者: Wall DP