As our training in biomedical informatics has become increasingly active in training individuals with different backgrounds (e.g., clinicians, neuroscientists, molecular biologists, and computer scientists) in a spectrum of different informatics areas, it has become important to define a set of core informatics topics that we can teach to all biomedical informatics trainees. These core topics can then be supplemented by elective courses and seminars tailored to a particular informatics area of interest. We believe that it is important that all trainees learn certain core informatics topics together for several reasons:
· The concepts underlying many core topics apply across all of biomedical informatics. It is important that trainees understand how these concepts can be applied in different informatics areas, and the particular issues that may arise in each area.
· The various informatics areas increasingly intersect with each other in research collaborations.
· All biomedical informaticians will need to work closely with one another throughout their careers, in teaching, in research, and in institutional computing activities.
This Core Curriculum provides all students with a solid introduction to a number of fundamental methodological topics that span many biomedical informatics and data science domains, as well as to the more specific methodological issues relating a) to clinical/clinical research informatics and b) to translational bioinformatics.
CBB 750: Core Topics in Biomedical Informatics and Data Science is co-taught by Profs. Brandt and Cheung. This course focuses on providing an introduction to common unifying themes that serve as the foundation for different areas of biomedical informatics, including clinical, neuro-, and genome informatics. The course is designed for students with significant computer experience and coursework who plan to build databases and computational tools for use in biomedical research. Emphasis is on understanding basic principles underlying informatics approaches to interoperation among biomedical databases and software tools, standardized biomedical vocabularies and ontologies, biomedical natural language processing, and modeling of biological systems. The course also offers an introduction into Big Data-style analytics, including at scale computation with Hadoop and NoSQL databases.
CBB 740: Clinical and Translational Informatics is co-taught by Profs. Shiffman and Krauthammer. The course is open to undergraduates, medical students, and fellows. The audience is typically split between students with a clinical and those with a biological career focus and the topics are chosen to demonstrate commonalities of informatics across clinical and basic science. In the first half of the course, Profs. Shiffman and Krauthammer examine biomedical data acquisition, storage and use, standards, decision making, and evaluation from both perspectives. The second part of the class is dedicated to exploring applications of these basic topics , including 1) design, function and evaluation of clinical information systems, 2) clinical decision making and guidelines, 3) clinical decision support systems, 4) informatics support of clinical research, 6) issues in defining the clinical phenotype, and 10) topics in translational bioinformatics such as genome-wide association (GWAS) studies, Whole Genome and Exome Sequencing (WGS/WES) data analysis, and analysis of genomic patterns of methylation, in relation to health and disease.
CBB 752: Bioinformatics: Practical Application of Simulation and Data Mining is taught by Prof. Gerstein. Topics include: Techniques in data mining and simulation applied to bioinformatics, the computational analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. Sequence alignment, comparative genomics and phylogenetics, biological databases, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, microarray normalization, and machine-learning approaches to data integration.
For the current courses and descriptions please follow the link to Yale University Degree-Granting Departments and Programs.
Elective Advanced Coursework
Yale offers a range of other courses that are potentially relevant to our trainees. These include courses offered by the Departments of Computer Science, Statistics, and Epidemiology and Public Health, in addition to the many biological and biomedical departments. In virtually all cases, these courses can either be taken for credit or audited depending on the needs and desires of the trainee. Training-grant supported CBB students choose from electives in their respective Translational or Biomedical Data Science Focus tracks.
Training Related to Career Development, Laboratory and Project Management, Interdisciplinary Training, and Training in Rigorous, Reproducible Research In Biomedical Informatics
All students and postdocs need to be familiar with topics and concepts related to academic development, management, team science and scientific rigor and reproducibility. We are teaching these concepts in a bi-weekly Biomedical Informatics Teaching Seminar that trainees attend, providing informatics content that does not fit into a formal course structure. The seminar’s content has been taught, refined and updated for more than 20 years, and currently contains sessions on:
- Career and professional development,
- · Laboratory and project management training,
- · Interdisciplinary training and team science,
- · Rigorous Experimental Design and Transparency to Enhance Reproducibility,
- · Enhancing communications skills: verbal presentations, preparation of abstracts and papers, effective slide making,
- · NIH grants: preparing a biomedical informatics research grant proposal, review of research grant applications at the NIH,
- · Group feedback on individual research projects, and The seminar also hosts external and internal invited speakers. (Recent speakers have been from Stanford, Carnegie Mellon, UCSD, University of Utah, Baylor and Columbia)
Selected topics with additional teaching resources that are available to trainees
· Career and professional development: The YCMI seminar includes regular faculty talks teaching students and fellows about key strategies for moving their careers forward, focusing on teaching, publication, professional societies and other outreach activities, using individual faculty’s careers as example. In addition, trainees are provided with regular opportunities to present their work to their labs and to members of our training program (with faculty feedback), which also serves as preparation for the career transition that occurs at the end of training. Profs. Brandt, Krauthammer, Shiffman, and Gerstein also host group sessions and individual one-on-one sessions focused on issues of career development tailored to the individual needs of trainees. The Yale Office of Postdoctoral Affairs partners with the Office of Career Strategy to offer additional assistance to all postdocs and predocs. Further, the Yale's McDougal Graduate Student Center provides graduate training workshops in writing and teaching. Our formal strategy for monitoring individual trainee’s academic development and progress is presented below under C.8 Trainee Guidance, Monitoring, and Evaluation.
· Laboratory and project management training: YCMI seminar series will continue to focus explicitly on training related to laboratory and project management. These sessions will include both practical examples and presentations from various core faculty with descriptions of their labs and several complex research projects. For example, Prof. Justice has been running a multi-site study for 15 years and she typically presents her approach, how she developed her project management plan, and how she adheres to keeping the project on track. The summer courses described below also provide hands on training in these areas targeted more for predocs. These sessions will complement training on these topics provided by the Yale Center for Research Computing and the Yale Center for Science and Social Science Information (see Short Courses below).
· Interdisciplinary training and team science: Our program is inherently interdisciplinary, and there is a need to prepare students and fellows for working in teams that are increasingly bigger and span more disciplines. Examples include large-scale sequencing efforts for cancer driver discovery (Yale SPORE in skin cancer) that involve computational (sequence data analysis and interpretation) and molecular biology (functional validation) labs, but also surgeons (source of samples), oncologists (outcome and treatment information), pharmacologists (structural aspects), geneticists (sequencing aspects), biostatisticians (data analytics) and others. Students should be familiar with the key concepts for making such large collaborations a success, including developing shared cognitive schemas, and adoption of common terms, processes, tools, methods, and procedures to advance their research. One of the key goals is that team members with varying roles and responsibilities, including those involved in database maintenance, statistical analysis, and data collection, are fully embedded in the team and develop a shared ownership for the research. Another aspect is management of team science initiatives and the identification of reasonable milestones for success in interdisciplinary team-based research. We are teaching these concepts in the Biomedical Informatics Teaching Seminar. We have invited speakers to help with this topic in the past, and will make this a regular event going forward. Prof. Jacob Tebes will give an overview of the current state of knowledge in interdisciplinary team science and offer practical strategies and approaches for team-based research (see LOS). Also, to prepare students to working with medical professionals, the Yale graduate school offers a Medical Research Scholars Program that bridges barriers between traditional predoctoral and medical training by providing Yale Ph.D. students with both medically oriented coursework and a mentored clinical experience. We have had 2 trainees attend this training.
· Research reproducibility: Finally, the University is preparing an approach to address training in rigorous experimental design and transparency to enhance reproducibility more broadly, but we are adding sessions on this topic for the Fall 2016 seminars where we will use the NIH Reproducibility Training Modules  with discussion led by faculty and postdocs. We plan to include topics of bias, blinding, and exclusion criteria, and discuss importance of - rigor of the previous experimental designs, full transparency in reporting experimental details, consideration of relevant biological variables, and authentication of key biological and/or chemical resources. A portion of this topic is also incorporated into our summer and fall 2016 short courses.
Good programming practices and the use of version control systems are an important component of project management and reproducibility. The Yale Center for Research Computing offers several relevant short courses including an introductory course on using Yale’s high performance computing resources and another on scripting with Python. In addition, the Yale Center for Science and Social Science Information offers many 2-hour workshops on topics including Matlab, research data management, and data visualization best practices. As part of our collaboration with the Center for Extended Data Annotation and Retrieval (CEDAR), one of the NIH BD2K Centers of Excellence, we designed a 1-day training program on metadata management and standards for large scale experiments that will be offered as part of a summer school being organized at Yale for June 2016 (Summer School on Computational Immunology at Yale, Prof. Kleinstein organizer). CBB graduate students have also recently decided to formalize the process by which the more senior students pass down their acquired, practical knowledge to the incoming students. This will be accomplished through yearly 1-day data science “bootcamp” that will be student-run. The first one is being organized for Fall 2016, and will cover five topics: Unix, HPC, version control, Python and R.
Predoctoral Training in Responsible Conduct of Research All pre-doctoral trainees in Yale’s BBS program (including all of our CBB trainees) are required to take a for-credit seminar that focuses on research ethics. Pre-doctoral students take CBB 601 “Responsible Conduct of Research,” an eight session course led by senior faculty in the biological and biomedical sciences, including CBB faculty. Documentation of completion is recorded on the student’s transcript. Student evaluations of the course are compiled by the University. Advanced doctoral students are also required to have a second round of training in responsible conduct of research. The BBS program offers a required course titled B&BS 503, RCR Refresher. This course is supplemented by additional sessions held by each graduate program for their students.
Postdoctoral Training in Responsible Conduct of Research To conform to NIH and NSF guidelines, Yale offers an 8 hour seminar for all post-doctoral fellows. This is given in May and June each year. All of our post-doctoral fellows are required to participate in this course during their fellowship period, after which they receive a certificate of completion.
Other Training Activities
In addition to taking courses, and carrying out Biomedical Informatics research projects, fellows may become involved in a number of other activities:
· Ongoing Biomedical Informatics projects typically involve a weekly or biweekly group meeting of the participants, where the status of the project is discussed, any problems are identified, and potential solutions and future plans are discussed. Fellows are encouraged to attend these weekly meetings for one or more projects in which they are not directly involved. This experience helps provide the fellow with a broader perspective on how interdisciplinary Biomedical Informatics research is performed. Biomedical Informatics faculty are involved in a variety of institutional computing initiatives in both the clinical and bioscience arenas. Fellows are encouraged to participate, which gives them experience in helping carry out applied computing projects within an academic center.
· Fellows are encouraged to assist in teaching activities to give them experience in presenting Biomedical Informatics material in a didactic setting.
· Although no patient care is required, we encourage each fellow who has clinical training to participate in clinical activities. This arrangement helps maintain clinical skills, allows the fellow to feel part of Yale on a clinical level, and can serve as the context for developing project ideas and productive clinical collaborations.