printer friendly pageBIODS 202: An overview of Biomedical Data Science (BIOMEDIN 202, BIOMEDIN 202P)
This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination. Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained. Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings. Next, Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies. Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities. Final
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This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination. Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained. Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings. Next, Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies. Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities. Finally, Module 4 will focus on reproducibility, evaluation and ethical issues when deploying models based on biomedical data, with emphasis on translation to practice. Emphasis will be placed questions, data and methods that advance health and medicine. Primary learning goals for this course include how to frame biomedical health questions, what data are needed to answer those questions, and what methodological constructs can be leveraged to probe and answer those questions. This course is a newly designed course for the PhD program of the Department of Biomedical Data Science but open to all. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Win
| Units: 3
Instructors:
Gevaert, O. (PI)
;
Wall, D. (SI)
;
Boen, J. (TA)
;
Feng, K. (TA)
;
Huynh, M. (TA)
;
Thapa, R. (TA)
BIODS 205: Bioinformatics for Stem Cell and Cancer Biology (STEMREM 205)
For graduate and medical students. High-throughput technologies and data science are essential tools in modern stem cell biology and cancer research. Students will gain practical exposure to bioinformatics concepts and techniques required to address biological questions within these research areas. The beginning of the quarter is focused on foundational principles underlying bioinformatics and genomics. Focus for the remainder of the quarter is on direct, hands-on experience with applications to common research problems. Topics include analysis of bulk and single-cell sequencing data, single gene to whole-genome analysis, machine learning, and data visualization. Intended for biology students without a background in computer science, or for students in a quantitative discipline interested in gaining exposure to key challenges in stem cell and cancer genomics. Basic programming experience is recommended but not required.
Terms: Win
| Units: 3
| Repeatable
2 times
(up to 4 units total)
Instructors:
Newman, A. (PI)
BIODS 219: Team Science Training for the Practicing Data Scientist (EPI 256)
The practice of data science is inherently a collaborative endeavor, and this course aims to equip data scientists and aspiring data scientists with the necessary skills for effective teamwork in clinical and translational research. The principles discussed in the course apply not only to the medical field but also to the behavioral and social sciences. Throughout the course, participants will explore various aspects of team engagement that are critical for the entire translational research process from study design to data management to data analysis to dissemination of findings. Key questions addressed include how to identify the required expertise for a team, how to integrate and onboard data scientists into teams, how to engage collaborators effectively by successfully leveraging multiple disciplines to jointly solve problems, how to educate the team on the role of the data scientist, as well as the data scientist's role in data collection, cleaning, and management. The course also
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The practice of data science is inherently a collaborative endeavor, and this course aims to equip data scientists and aspiring data scientists with the necessary skills for effective teamwork in clinical and translational research. The principles discussed in the course apply not only to the medical field but also to the behavioral and social sciences. Throughout the course, participants will explore various aspects of team engagement that are critical for the entire translational research process from study design to data management to data analysis to dissemination of findings. Key questions addressed include how to identify the required expertise for a team, how to integrate and onboard data scientists into teams, how to engage collaborators effectively by successfully leveraging multiple disciplines to jointly solve problems, how to educate the team on the role of the data scientist, as well as the data scientist's role in data collection, cleaning, and management. The course also delves into issues that impact rigor and reproducibility such as authorship, reasonable timelines, interpreting empirical findings, and the importance of statistical analysis plans and study registration. Material is taught through lectures, simulated role-playing exercises, and real-time demonstrations to enhance learning and practical application. Data scientists are working more and more as part of scientific teams. In this course, participants who are (or who are training to be) data scientists will learn optimal team science tools for engaging clinical and translational investigators in the collaborative research process. These principles apply across the medical, behavioral, and social sciences.Topic areas include: optimal team make up from a data science perspective; how to engage collaborators on study design; how to educate collaborators on engaging data scientists; how to educate collaborators on rigor and reproducibility principles such as creating a statistical analysis plan, pre-registering studies, and deciding on authorship; elements that comprise the ideal statistical analysis plan; how to play an integral role during data collection and data extraction phases of the study; and optimal approaches for dissemination of findings to the team and to the research community that adhere to rigor and reproducibility principles and that ensure integration of the data scientist?s voice. In addition to lectures, materials will be taught using simulated role playing and real-time demonstrations of collaborations.
Terms: Win
| Units: 2
Instructors:
Desai, M. (PI)
BIODS 232: Consulting Workshop on Biomedical Data Science
The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devot
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The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devoted to drop-in consulting. DBDS faculty are available to provide assistance with your research questions. Skills required of practicing biomedical consultants, including exposed to biomedical and health science applications, identification of data science related questions, selection or development of appropriate statistical and analytic approaches to answer research needs. Students are required to attend the regular workshops and participate one to two consulting projects as team members under the supervision of faculty members or senior staff. Depending on the nature of the consulting service, the students may need to conduct numerical simulation, plan sample size, design study, and analyze client data. the formal written report needs to be completed at the end of consulting projects. May be repeated for credit. Prerequisites: course work in applied statistics, data analysis, and consent of instructor.
Terms: Aut, Win, Spr
| Units: 1-2
| Repeatable
2 times
(up to 4 units total)
BIODS 235: Best practices for developing data science software for clinical and healthcare applications
Best practices for developing data science software for clinical and healthcare applications is a new seminar aimed to provide an overview of the strategies, processes, and regulatory hurdles to develop software implementing new algorithms or analytical approaches to be used in clinical diagnosis or medical practice. Upon completing this seminar, biomedical scientists implementing diagnostics, analytical, or AI-driven clinical decision support software should better understand how to protect, transfer, commercialize, and translate their inventions into the clinic. Topics include: Intellectual property strategies and technology licensing challenges; software development and quality best practices for the clinic; regulatory frameworks for clinical decision support and diagnostics informatics applications. It is open primarily to graduate students across Stanford and combines short lectures, guest industry speakers, and workshop sessions to allow participants to receive feedback on curren
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Best practices for developing data science software for clinical and healthcare applications is a new seminar aimed to provide an overview of the strategies, processes, and regulatory hurdles to develop software implementing new algorithms or analytical approaches to be used in clinical diagnosis or medical practice. Upon completing this seminar, biomedical scientists implementing diagnostics, analytical, or AI-driven clinical decision support software should better understand how to protect, transfer, commercialize, and translate their inventions into the clinic. Topics include: Intellectual property strategies and technology licensing challenges; software development and quality best practices for the clinic; regulatory frameworks for clinical decision support and diagnostics informatics applications. It is open primarily to graduate students across Stanford and combines short lectures, guest industry speakers, and workshop sessions to allow participants to receive feedback on current related projects that are undertaking. Enrollment limited to 25 to allow participants present their current projects. Prerequisites: Basic experience in programing and algorithm or software tool development. Ideally, the participant is actively implementing a new method/process/application in software aimed to be used in the clinic.
Terms: Win
| Units: 1
Instructors:
De La Vega, F. (PI)
;
Klein, T. (PI)
BIODS 251: Causal Inference in Clinical Trials and Observational Studies (STATS 251)
This course offers an overview of statistical foundation for modern clinical trial design in precision medicine research. Starting from a quick review of traditional clinical development paradigm through Phase I to III clinical trials for medical product approval and Phase IV post-marketing studies for safety evaluation, and challenges in the time and society costs, we will introduce recently developed innovative designs and their statistical methodology across all phases of clinical trials. You expected to learn the statistical considerations for novel phase I-II trial designs, master protocols for umbrella, platform and basket trials, adaptive and enrichment designs including subgroup selections, estimand, surrogate and composite endpoints, integration of real-world evidence and patient-focused medical product development, and meta-analysis of clinical trial endpoints. Prerequisites: Working knowledge of statistics and R.
Terms: Win
| Units: 3
BIODS 253: Software Engineering For Scientists
The importance of software to science has grown tremendously over the past 20 years. Proper use of standardized Software Engineering techniques, such as cloud computing, testing, virtualization, testing, and source control, is often necessary for high-quality, and replicable science. Software Engineering for Scientists is designed to help researchers, scientists, and non-domain-experts gain hands-on knowledge of the tools and practices that will make your day-to-day work more efficient and less error-prone, be it in academia or industry. Students will learn to adopt the most important and germane of the techniques used in the real world (from startups to large companies), and we will provide you with a good understanding of the tools, approaches, and tradeoffs inherent in writing any kind of program. The class is taught by an expert with 20 years of experience building software, managing engineering and product teams at companies including Google and Twitter who now works primarily in
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The importance of software to science has grown tremendously over the past 20 years. Proper use of standardized Software Engineering techniques, such as cloud computing, testing, virtualization, testing, and source control, is often necessary for high-quality, and replicable science. Software Engineering for Scientists is designed to help researchers, scientists, and non-domain-experts gain hands-on knowledge of the tools and practices that will make your day-to-day work more efficient and less error-prone, be it in academia or industry. Students will learn to adopt the most important and germane of the techniques used in the real world (from startups to large companies), and we will provide you with a good understanding of the tools, approaches, and tradeoffs inherent in writing any kind of program. The class is taught by an expert with 20 years of experience building software, managing engineering and product teams at companies including Google and Twitter who now works primarily in biotechnology and a Professor in the Biomedical Data Sciences with more than 30 years of experience teaching in bioinformatics at both Stanford and UCSF. Pre-recorded lectures will be provided and will cover topics from both a theoretical and practical perspective. In person lectures will be primarily interactive; we will spend time answering students' questions and talking about how these learnings could be useful to their research. There will be a number of assignments and a final project which can be based on students' existing research.
Terms: Win
| Units: 2
Instructors:
Klein, T. (PI)
;
Pandurangan, V. (SI)
BIODS 260B: Workshop in Biostatistics (STATS 260B)
Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student
Terms: Win
| Units: 1-2
| Repeatable
for credit
BIODS 296: Surfing the Waves of Data (DATASCI 194W, DATASCI 294W)
This course is designed to bring students into the world where music, quantum computing, wavelets, and DNA converge through the lens of wave data analytics. This interdisciplinary course explores the profound connections between these seemingly disparate fields, all united by the fundamental principles of wave theory.In the realm of music, students will discover how sound waves create harmony and rhythm, unveiling the mathematical beauty behind musical compositions. Transitioning into the quantum domain, the course will delve into the enigmatic behavior of quantum waves, offering insights into the cutting-edge technologies of quantum computing and their potential to revolutionize data processing.The journey continues with wavelets, powerful mathematical tools that decompose complex signals into simpler components. Students will learn how wavelets are employed in diverse applications, from image compression to signal processing, and their pivotal role in modern data analytics.Finally, t
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This course is designed to bring students into the world where music, quantum computing, wavelets, and DNA converge through the lens of wave data analytics. This interdisciplinary course explores the profound connections between these seemingly disparate fields, all united by the fundamental principles of wave theory.In the realm of music, students will discover how sound waves create harmony and rhythm, unveiling the mathematical beauty behind musical compositions. Transitioning into the quantum domain, the course will delve into the enigmatic behavior of quantum waves, offering insights into the cutting-edge technologies of quantum computing and their potential to revolutionize data processing.The journey continues with wavelets, powerful mathematical tools that decompose complex signals into simpler components. Students will learn how wavelets are employed in diverse applications, from image compression to signal processing, and their pivotal role in modern data analytics.Finally, the course will explore the fascinating world of DNA, where genetic information is organized in wave-like patterns. By examining the wave properties of DNA sequences, students will gain a deeper understanding of genetic coding, mutation, and the intricate dance of life at the molecular level.Through a blend of theoretical foundations and practical applications we will have projects where students will analyze wave data and get a sense as to what can be done from an analytical perspective.
Terms: Win
| Units: 4
Instructors:
Rivas, M. (PI)
BIODS 299: Directed Reading and Research
For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
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