Develop machine learning solution with real-world healthcare data
The 2nd SBMI Healthcare Machine Learning Hackathon is calling capable and motivated undergraduate and graduate students from Gulf Coast Consortia institutions and other Houston area universities. Come join us for this great opportunity to challenge your coding skills, meet new people, and enjoy the gathering of young hackers. This 24-hour Hackathon is organized by the Center for Secure HEalthcare Machine Learning (SHEL) at the School of Biomedical Informatics in UTHealth. The event is sponsored by Vir Biotechnology, for a prize of $1,200 for the winner. Undergraduate and graduate students from the institutes within the Gulf Coast Consortia (include UTHealth, MDACC, UH, Rice, TAMU, UTMB, IBT, and Baylor) and colleges in the vicinity of TMC are highly encouraged to apply. Details on this Hackathon can be found in this official link. Click here to register.
Undergraduate and graduate students (1st or 2nd year) from the institutes within the Gulf Coast Consortia (include UTHealth, MDACC, UH, Rice, TAMU, UTMB, IBT, and Baylor) and colleagues in the vicinity of TMC are highly encouraged to apply.
Identifying new treatment for diseases is the long-standing goal of medicine. The cost of drug discovery is very high in the traditional setting. Systematic integration of previous results and knowledge might change the game by identifying highly promising drugs to save cost and speedup discovery. High-throughput drug screening using computational approaches has the potential to substantially improve cost-efficiency by automatically estimating drug sensitivity based on genomic and pharmacological data. These computational methods utilize drug sensitivity data at certain cell lines and predict promising drugs that potentially have high sensitivity in other cell lines. In this Hackathon, participants are asked to build a prediction model that ranks promising drugs in given cancer cell lines.
The goal is to develop machine learning solution to predict drug’s sensitivity in given cell lines. Participants should rank drugs that are likely to be sensitive (i.e., relative inhibition > 50%) in cell lines given in test sets. A key challenge is that testing cell lines have limited experimental drug response data in the training set. Participants are encouraged to use relative dense observations from other types of cancer cell lines to predict this data-rare cancer cell lines.
- Machine Learning/ AI