Machine Learning Methods for Single Cell RNA-Sequencing Data to Improve Clinical Oncology
Name
boiarsky-rboiar-phd-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
24.12 MB
Format
Adobe PDF
Checksum (MD5)
15276c6296260ad73600c56ec3dd7fa9
Author(s)
Boiarsky, Rebecca
Advisor(s)
Sontag, David
Getz, Gad
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Single-cell RNA sequencing (scRNA-seq) offers a detailed view of the cellular and phenotypic composition of healthy and diseased tissues. While machine learning (ML) methods are well-suited for the high-dimensional nature of scRNA-seq data, current computational tools face limitations, particularly when confronted with data from clinical oncology. This thesis presents the development and application of ML techniques for scRNA-seq data to address key computational challenges, with a focus on challenges in clinical oncology. It covers four key areas: identifying gene signatures and biomarkers in multiple myeloma, developing methods to account for somatic copy number variations in tumor samples, benchmarking large, pre-trained scRNA-seq foundation models, and creating a framework for predicting clinical outcomes using patient-level representations of single-cell data. Together, these studies aim to develop and evaluate novel ML algorithms for scRNA-seq data which can unlock actionable insights for personalized medicine.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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