AOC: Precision Medicine and Computational Biology
Students who choose the Precision Medicine and Computational Biology AOC may either be specifically interested in helping develop new Precision Medicine approaches perhaps based on epigenomics, single cell omics, cell-free DNA; develop predictive models using machine learning and artificial intelligence; leverage data from sensors to predict and model health outcomes; develop applications and pipelines that will directly aid patient care or illuminate disease processes, or may have an area of investigation that falls into the AOC, or may specifically wish to work with a faculty member who is a member of this AOC faculty.
As Precision Medicine and Computational Biology are diverse in their manifestations, the program will offer students exposure to the breadth of insights generated by computational methods, as well as develop expertise in a niche of their choosing. Students will engage in practical and academic exercises that will further refine an individual area of focus within the AOC, which will then be expanded upon in a scholarly project under direct mentorship. Students will be encouraged to think broadly and from an interprofessional practice perspective.
Cross-disciplinary collaboration as part of the student’s activities and as the basis of the scholarly project will be highly encouraged where relevant.
Significance: Precision Medicine and Computational Biology have the potential to revolutionize the field of medicine. Adjacent disciplines, such as computer science and mathematics, have delivered a variety of new techniques that can have broad-ranging impacts in medicine, particularly surrounding the analysis of large datasets and prediction of clinical outcomes. The application of these techniques, as well as the development of improved methods, will continue to generate insights across the biomedical landscape, and as such, there are innumerable areas of fertile investigation.
Goals and Learning Objectives: To explore the potential of genomic testing, precision medicine and other modalities for patient care; to come up with new and improved approaches for precision medicine; to understand the wide range of applications for computational techniques in medicine; to understand basic techniques to design and/or implement a subset of these techniques; to understand the benefits and challenges of computational techniques as applied to medicine; to identify an area suitable for application of a computational technique, under the mentorship of a faculty member from the Institute for Computational Biomedicine or Institute for Precision Medicine at WCM or Tri-Institutional Program in Computational Biology & Medicine, or any other suitable faculty member; to acquire data for and implement computational technique toward further understanding of medical sciences, improved clinical decision-making, decreased cost, increased area under the receiver operating curve (AUC) for diagnostics, or decreased latency to results.
Core activities and/or Practical Experiences
- Attendance at computational biomedicine seminars at Weill Cornell Medicine, Memorial Sloan Kettering, Rockefeller University, and Hospital for Special Surgery (seminars would be as related to student’s interests). This includes the Institute for Computational Biomedicine Seminars (held semi-monthly and coordinated by Drs Elemento and Betel); the CME-accredited Institute for Precision Medicine Seminars (coordinated by Dr. Elemento, Rubin, and Beltran); the Machine Learning in Medicine Monthly Seminars (organized by Dr. Amy Kuceyeski)
- Attendance at Precision Medicine and Computational Biology meetings such as Friday 11 am meeting of the Computational Biology team of the Institute for Precision Medicine; group meetings of mentoring faculty members
- Attendance at lectures from related fields that relate to computational biomedicine (e.g. the use of machine learning in global health)
- Web-based educational material recommended by the faculty
- Career seminars with local and visiting faculty to learn about how they deploy and utilize computational methods to improve understanding of disease and better treat patients
- Journal clubs focusing on how computational methods are being deployed in biomedicine in the latest research
- Research conferences at Stanford, NIPS, and related events
Practical experience(s): the development of Precision Medicine and Computational Biology projects, with a specific emphasis on coding, said applications, including a collection of requisite data in a wet lab or in the clinic for patient data collection. For specific projects focusing on the deployment of projects that directly guide clinical decision-making, students will participate in the care of said patients that are beneficiaries of precision medicine pipelines.
- Building genomics-driven predictive models of disease risk and treatment response
- Predicting drug toxicity profiles with machine learning applied to chemical structures
- Predicting patient response to immunotherapy based on tumor genomic profile
- Improving diagnosis of prostate cancer by analysis of miRNAs in biopsy
- Discovering new purposes for FDA-approved drugs by means of receptor localization
- Mine genomics databases for novel drug targets
- Use text mining and NLP to identify relevant Pubmed articles
To find out more about AOC: https://medicaleducation.weill.cornell.edu/medical-education/md-program/areas-concentration
Dr. Olivier Elemento (email@example.com)
Dr. Harel Weinstein (firstname.lastname@example.org)