2026-09-19T07:43:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1637102025-11-18T03:03:21Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Sontag, David
Getz, Gad
Boiarsky, Rebecca
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-11-17T19:08:41Z
2025-11-17T19:08:41Z
2025-05
2025-08-14T19:36:34.260Z
https://hdl.handle.net/1721.1/163710
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.
Ph.D.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Machine Learning Methods for Single Cell RNA-Sequencing Data to Improve Clinical Oncology
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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<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.</Abstract>
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