Forest Yang

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2024-187

August 30, 2024

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-187.pdf

Large biological data, such as medical imaging and single-cell level genomic data, are rich sources of biological information. Machine learning is a tool to extract that information into a usable form, whether it be predictions for some prediction task or insights drawn from the model. We explore three applications of machine learning to biology. One is on using deep learning to perform metastatic cancer prognosis from CT images by predicting lesion-level risks. Next, we apply neural network interpretation techniques to understand how DNA shape affects transcription factor binding. Finally, we apply an existing gene regulatory network inference framework, CellOracle, in the context of reprogramming mature cells to pluripotent cells.

Advisors: Laurent El Ghaoui

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BibTeX citation:

@phdthesis{Yang:EECS-2024-187,
    Author= {Yang, Forest},
    Title= {Drawing Biological Understanding From Machine Learning},
    School= {EECS Department, University of California, Berkeley},
    Year= {2024},
    Month= {Aug},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-187.html},
    Number= {UCB/EECS-2024-187},
    Abstract= {Large biological data, such as medical imaging and single-cell level genomic data, are rich sources of biological information. Machine learning is a tool to extract that information into a usable form, whether it be predictions for some prediction task or insights drawn from the model. We explore three applications of machine learning to biology. One is on using deep learning to perform metastatic cancer prognosis from CT images by predicting lesion-level risks. Next, we apply neural network interpretation techniques to understand how DNA shape affects transcription factor binding. Finally, we apply an existing gene regulatory network inference framework, CellOracle, in the context of reprogramming mature cells to pluripotent cells.},
}

EndNote citation:

%0 Thesis
%A Yang, Forest 
%T Drawing Biological Understanding From Machine Learning
%I EECS Department, University of California, Berkeley
%D 2024
%8 August 30
%@ UCB/EECS-2024-187
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-187.html
%F Yang:EECS-2024-187