Understanding information at the biomolecular level using statistics and machine learning

Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Luu, Alan M.

Contributors dc:contributor

• Song, Jun
• Maslov, Sergei
• Golding, Ido
• Perez-Pinera, Pablo

• biology
• statistics
• machine learning
• genomics
• Central Dogma
• DNA
• RNA
• protein
• information
• genome editing
• CRISPR
• next-generation sequencing
• deep learning
• genetic engineering
• keratinocyte
• cancer
• immune system
• CRISPR base editor
• squamous cell carcinoma
• basal cell carcinoma
• RNA-Seq
• scRNA-Seq
• convolutional neural network
• deep metric learning
• multimodal learning
• T-Cell receptors
• neural network interpretation
• epitope
• MCMC

Statement dc:rights

• Copyright 2022 Alan Luu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115452

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/115452