Print Settings
 

BIODS 201: Biomedical Informatics Student Seminar (BIOMEDIN 201)

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.
Terms: Aut, Win, Spr | Units: 1 | Repeatable 3 times (up to 3 units total)

BIODS 206: Applied Multivariate Analysis (STATS 206)

Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre- or corequisite: 200.
Terms: Aut | Units: 3
Instructors: ; Owen, A. (PI); Li, H. (TA)

BIODS 221: Machine Learning Approaches for Data Fusion in Biomedicine (BIOMEDIN 221)

Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multi-omics, multi-modal and multi-scale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent, Stats 60 or equivalent.
Terms: Aut | Units: 2

BIODS 227: Machine Learning for Neuroimaging (PSYC 121, PSYC 221)

Machine learning has driven remarkable advances in many fields and, recently, it has been pivotal in enhancing the diagnosis and treatment of complex brain disorders. Biomedical and neuroscience studies frequently rely on neuroimaging as it provides non-invasive quantitative measurement of the structure and function of the nervous system. Machine and deep learning methods can, for example, refine findings for specific diseases or cohorts enabling the detection of imaging markers at an individual level. This, in turn, paves the way for personalized treatment plans. In this course, we explore the methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data and study novel, robust, scalable, and interpretable machine learning models for this purpose.Students have the option to enroll in the class for either 3 or 4 units. All students, regardless of their unit choice, are expected to attend every class session. The primary class content will cover the fundamentals of machine learning, offer some limited hands-on training, and explore the application of ML to neuroimaging. Those opting for 4 units will benefit from an extra hour of instruction weekly, diving deeper into core ML concepts and receiving extended hands-on training. The scheduling of this additional hour will be determined based on the availability of the students enrolled for 4 units to ensure a mutually convenient time slot. Undergraduate students and those who do not have ML backgrounds are advised to take the course for 4 units.
Terms: Aut | Units: 3-4
Instructors: ; Adeli, E. (PI); Pohl, K. (PI)

BIODS 228: Statistical Genomics for Planetary Health: Oceans, Plants, Microbes and Humans (BIO 206)

Data scientific analysis of genomics data has transformed biology, enabling myriad discoveries with enormous impacts on human and planetary health. Algorithms and statistics are central to knowledge of human and plant genomic variation, to microbiomes and carbon cycling in the ocean. This class will present the important open problems in the above application areas, pose them as statistical problems and explore core, unifying methods that are used to study them. We will cover diverse scientific application areas focusing on unifying ways they can be addressed statistics and informatics including (i) historical and computer-scientific approaches to addressing these problems where analysis begins with assembling and or aligning to a set of reference genomes (ii) 'statistics-first' approaches that operate on raw sequencing data to perform statistical inference for discovery. This class will present challenges and opportunities in using new methods that do not require a reference to illustrate how the planetary ecosystem can be investigated from a statistics-first perspective: from studies of microbial and plant life to humans. Motivation will be driven by current open and critical problems in planetary health, microbiome research and examples from human genomics. We will investigate statistical and informatic methods that can be used to address these problems including generalized linear models, Pearson's chi-square, permutation testing and present scientific examples/case studies where these tests fail to control the statistical level. Lectures will be pre-recorded with mandatory in-class discussions and problem sessions in class. Evaluation will be based on completion of ungraded problem sets with the major evaluation will be class projects.
Terms: Aut | Units: 3

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 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 260A: Workshop in Biostatistics (STATS 260A)

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: Aut | Units: 1-2 | Repeatable for credit

BIODS 290: Critical Exploration of Topics in Biomedical Data Science: Generative AI

Each edition of the course focuses on one topic of research or translation. Students read, present and discuss papers from the literature.
Terms: Aut | Units: 1 | Repeatable 4 times (up to 4 units total)
Instructors: ; Sabatti, C. (PI); Zou, J. (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

BIODS 399: Graduate Research on Biomedical Data Science

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit
© Stanford University | Terms of Use | Copyright Complaints