USC · computational 2024
From Wet Lab to Machine Learning: Predicting Sepsis Mortality
Transcriptomic patient data, computationally
The first major move from wet-lab research into computational biology: building and iterating machine-learning workflows to estimate sepsis mortality risk from transcriptomic patient data.
Completed TranscriptomicsMachine learningClinical data
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Why this one mattered
Everything before this had been physical — animals, plates, a bench. This project was the first major move from wet-lab research into computational biology.
Working with transcriptomic patient data, I helped build and iterate machine-learning workflows for estimating sepsis mortality risk, while learning how choices such as feature selection, validation strategy, and cohort differences can change what a model appears to know.
Methods
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