William Jarrard
I am currently a Computational Biologist and Machine Learning Researcher at UCSF, where I work in the Feng/Quigley Lab on applying deep learning to study transcription factors in prostate cancer. I was previously an undergraduate at the University of Virginia (UVA), where I majored in Systems Engineering. During my time at UVA, I made significant contributions to cancer research, particularly in advancing our understanding of genetic factors in oncology. I also completed a 4+1 program to earn my Master’s in Data Science at UVA.
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Projects and Papers
Studied how UTX, a histone demethylase and tumor suppressor, relies on phase separation into liquid condensates for its chromatin-regulatory activity. Loss of its intrinsically disordered region (cIDR), common in cancer mutations, disrupts this process, impairing tumor suppression and chromatin interactions.
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Developed a method for extracting crucial information, like drug names, from paper medical sheets. By utilizing YOLO for segmentation and LSTMs to capture the sequential nature of drugs, out method achieved impressive accuracy rates. This project has the potential to streamline the medical record-keeping process and improve patient care.
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Investigating transcription factor colocalization in prostate cancer using an innovative deep learning model to understand the increased aggressiveness of RB1/TP53 double knockout cases. This project is currently in progress.
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