Results for BIODS

Participants report on recent articles from the Biomedical Informatics literature or their research projects. Goals are to teach critical reading of scientific papers and presentation skills. Summer Quarter consists of critical review of relevant literature led by faculty associated with the Biomedical Informatics Program. May be repeated three times for credit.

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.

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.

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Experts in the field will present guest lectures. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.

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

This project-based course delves into the cutting-edge of Generative Artificial Intelligence (AI) and its transformative applications in the healthcare domain. As technology continues to evolve, so does the potential for AI to revolutionize healthcare practices, from diagnostics to personalized treatment plans. Participants will learn about the latest advances in Generative AI, exploring state-of-the-art models and techniques tailored for healthcare challenges. Students will be introduced to Human-Centered Design methodology -- involving empathy and needs finding, prototyping and iteration. Class projects will focus on deployment of Generative AI using datasets such as population biobanks, and training models from these population-scale datasets. Key topics covered include the utilization of Generative AI in medical image synthesis, enhancing diagnostic capabilities, and using genomes and protein language models for variant effect prediction. The course also navigates the ethical considerations surrounding the use of generative models in healthcare, addressing issues of privacy, bias, and interpretability. Through a combination of theoretical insights and hands-on practical sessions, participants will gain a deep understanding of how Generative AI is reshaping the healthcare landscape, and how they could have a positive impact. Guest speakers from venture capital and industry with real-world examples will illustrate successful applications of generative models in medical imaging, drug discovery, and patient care, and discuss the challenges they see in translation from research to implementation.Students will need to visit the link and fill out an application before they get a reg. code to register for course. https://dschool.stanford.edu/classes/generative-ai-for-healthcare

For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor.

A seminar-style course with lectures on a range of computational topics important for modern data-intensive science, jointly supported by the Statistics department and Stanford Data Science, and suitable for advanced undergraduate/graduate students engaged in either research on data science techniques (statistical or computational, for example) or research in scientific fields relying on advanced data science to achieve its goals. Seminars will alternate a presentation of a topic, usually by an expert on that topic, typically leading to exercises applying the techniques, with a follow up lecture to further discuss the topic and the exercises. Prerequisites: Understanding of basic modern data science and competence in related programming, e.g., in R or Python. https://stats352.stanford.edu/

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.